Jobs

We are hiring for 27 PhD positions before mid 2020. If you are interested in any of these positions, get in touch with the relevant team member mentioned below. For further details about how to apply and deadlines please see below.

Before you apply, try to find out more about our research: watch our video, read our research page, read what the media wrote about our work, read the public version of our research plan, and look at the publications of the relevant team members.

PhD Positions open now
PhD positions opening soon

Contact: A.L.Robbins-vanWynsberghe@tudelft.nl

Research Project Description
Many current AI systems are implicitly supporting or constraining human moral judgment, e.g. information on Facebook, recommendation systems, targeted advertising. The goal of this project is to make this support (or constraint) explicit as a step towards empowering better human moral judgments. Our working assumption is that AI should support but not take over human moral judgement and decisions. To determine the desirable role of AI support, we should first of all better understand human moral judgement, and particularly the importance of context in it. Two case studies will be carried out on how AI may support moral human judgement in the context of changing values. On the basis of these, desirable (and undesirable) forms of AI support will be identified, and insights will be translated into a number of design principles for AI support for human moral judgement.

Job Requirements
We seek an energetic PhD candidate, to carry out this research project at the forefront of the interdisciplinary combination of ethics and artificial intelligence. The candidate has a master’s degree or equivalent in ethics, philosophy, or STS. During the project you are expected to write the research articles that together will form the basis of a thesis to attain a PhD degree (dr.) at Delft University. Candidates will participate in the education and supervision program of the Delft Graduate School as well as in the Hybrid Intelligence project graduate school. Teaching duties of max 10% of the time are foreseen.

To apply for this position click here [link to academic transfer jobs

Contact: c.m.jonker@tudelft.nl

Research Project Description
Perspectives in deliberation is about developing Artificial Intelligence techniques to find the underlying structures of debates and group deliberations with the idea that we can help participants to a debate / deliberation to understand why others have a different opinion in this debate. To this end we want to use computational linguistics to extract what we call micro-propositions from text: proposition mining. Secondly, we want to model the implications for stakeholders of these propositions: implication mining. Thirdly, we want to extract and understand the perspectives of the stakeholders on these implications: perspective mining.

In addition to understanding the debate and the perspectives, the AI you develop will seek to interact with stakeholders about the interpretations of what they brought in as statements and arguments. 

Job Requirements
Who wants to build AI that understands people so well that it can help resolve debates and conflicts? Who wants  to build a new type of AI that we call Hybrid Intelligence, in which AI and people can deliberate on societal changes and their impact.

Participate in the HI project, its meetings and its training. We seek an energetic PhD candidate, to carry out this research project at the forefront of the interdisciplinary combination of artificial intelligence and computational linguistics. The candidate has a master’s degree or equivalent in artificial intelligence or computational linguistics. During the project you are expected to write the research articles that together will form the basis of a thesis to attain a PhD degree (dr.) at Leiden University. Candidates will participate in the education and supervision program of the Leiden Graduate School of Science as well as in the Hybrid Intelligence project graduate school. Teaching duties of max 10% of the time are foreseen.

To apply for this position click here [link to university job site

Contact: d.p.balliet@vu.nl 

Research Project Description
This PhD project will be a collaboration between psychology and computer science to develop hybrid intelligence that can understand, predict, and potentially aid in initiating collaborative behavior. To build such machines, we must further develop our understanding about how people select their partners and initiate collaborations. Moreover, this project will involve a close collaboration between psychology and computer science, and will apply state-of-the-art methods and techniques in both computer science and psychology to advance our understanding about these issues.

Partner selection is fundamental to understanding how people initiate cooperative relations and to avoid being exploited by non-cooperative individuals – two key features of human sociality thought to underlie why humans are such a cooperative species (see Barclay, 2013). This research will test and develop theory about how people choose cooperative partners. The project will use both naturalistic settings (e.g., social networking at scientific conferences) and experimental settings (e.g., experimental group tasks), to examine the non-verbal and verbal behaviors during social interactions that can be used to predict whether people choose to select another person (or not) as a collaboration partner. During these studies, participants will wear multi-modal sensors and be video recorded while interacting with other people for the first time, which will be used to capture non-verbal and verbal behaviors that can predict how people evaluate their interaction partner (e.g., their traits, motives), the social interaction (e.g., the closeness, social power), and behavior motivations (e.g., avoiding versus approach the person in future interactions).

This PhD project is a collaboration between Psychology (Daniel Balliet) and Computer Sciences (Hayley Hung, Rineke Verbrugge), and the PhD will also work closely with another PhD student supervised directly by Hayley Hung. The candidate should have an openness to working in a multi-disciplinary team, and have a general interest in establishing a closer connection between psychology and the computer sciences.

Job Requirements
• Research Master or Master in Psychology or closely related discipline (economics, sociology)
• Research traning and experience;
• Excellent knowledge of methodology and statistics;
• The intention to work in a multi-disciplinary team;
• An affinity with the related computer science research;
• Excellent command of written and spoken English;
• Good knowledge of office and statistical software (MS Office, SPSS, R).

To apply for this position click here [link to university job site] or [link to academic transfer jobs]

Contact: d.grossi@rug.nl 

Research project description
Deliberation is a key ingredient for successful collective decision-making. In this project we will:

– investigate the mathematical and computational foundations of deliberative processes in groups of autonomous agents.
– lay the theoretical groundwork for modelling and analyzing how opinions are exchanged and evolve in groups of autonomous decision-makers.
– develop principled methods for the design of deliberation procedures that can provably improve the quality of group decisions.

Job requirements
The successful candidate should have/be:

– a motivated PhD student, with a keen interest in pursuing fundamental and interdisciplinary research at the interface of (computational) social choice theory, game theory, computational intelligence, and multi-agent systems.
– a master’s degree or equivalent in artificial intelligence, computer science, mathematics, or theoretical economics.
– Good analytical skills and a positive attitude towards interdisciplinary work.

To apply for this position click here [link to university job site

Contact: f.p.m.dignum@uu.nl 

Research Project Description
At Utrecht University, we are looking for several PhD students within the Hybrid Intelligence project. For the position on the computational theory of mind we look for a PhD student to investigate and develop a computationally efficient theory of mind that is theoretically sound.
The research will focus on which type of general knowledge on other agents has to be kept in order to keep a usable theory of mind available. How much of the historical information and premises on preferences, goals and motivations should be taken into account? The premise is that the theory of mind that needs to be kept depends on the level of interaction that is aimed for and the context in which this interaction takes place. You will apply the developed theory in the area of collaborative privacy. A typical situation in collaborative privacy is that a user in a collaborative system, such as an online social network, would like to share content that could possibly be co-owned by others, such as group pictures or collaboratively edited documents. At the time of sharing, the user has to take into account how the shared content can or will be used and how this sharing would affect other users and take an action accordingly. By employing a computationally viable model of theory of mind, a user can reason about other co-owners’
privacy expectations on the content in a given context using a theory of mind and make a sharing decision based on that.
The PhD candidate’s principal duty is to conduct scientific research, resulting in a PhD thesis at the end of the appointment. Other duties may include supporting the preparation and teaching of Bachelor’s and Master’s level courses, supervising student theses, managing research infrastructure and participating in public outreach. This position presents an excellent opportunity to develop an academic profile.

Job Requirements
The successful candidate is an excellent student and holds a Master’s degree in Computer Science, Artificial Intelligence, or Math. A strong background in formal logic or formal modeling as well as fluency in programming is essential. Excellent English communication skills are necessary.

To apply for this position click here [link to university job site]  

Contact: a.e.eiben@vu.nl 

This position is already filled

Contact: h.c.vanhoof@uva.nl

Research Project Description
At the university of Amsterdam, we are looking for a PhD candidate interested in combining (deep) reinforcement learning research with prior knowledge from knowledge graphs to learn explainable strategies for complex sequential tasks.
Many problems of practical interests are concerned with optimising sequential decision making. Think, for example, of finding optimal trajectories for vehicles or robots or deciding which medical tests to run. Methods for classical planning based on symbolic representations are typically explainable to human collaborators but rely on an exact problem description; while data-driven (e.g. reinforcement learning) approaches do not rely on a provided problem specification, but are data hungry and inscrutable. The combination of the complementary strengths of these approaches is expected to advance the state of the art in the areas of knowledge representation and reinforcement learning.

Job Requirements
We are looking a candidate in possession of a Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field. You have a strong scientific and mathematical background in artificial intelligence. Familiarity and interests with knowledge representation or reinforcement learning is preferable. You should have a good academic record and eagerness to tackle complex scientific problems and be able to implement and evaluate learning algorithms, e.g. using Python deep learning toolkit. Finally, you are able to work well in teams and communicate fluently in written and spoken English.

To apply for this position click here [link to university job site] or [link to academic transfer jobs

Contact: hh@liacs.nl 

Research Project Description
The goal of this PhD project is to develop automated machine learning methods for changing/non iid data, with applications to standard learning scenarios as well as to automated negotiation. Such techniques are key to enabling the efficient and robust use of machine learning systems and components for a broad range of human-centred AI applications. They will also contribute to fundamental advances in machine learning. Automated negotiation scenarios are of particular interest, as they play a key role in systems dealing with potentially conflicting interests between multiple users or stakeholders.

Job Requirements
We are looking for a candidate with an MSc degree in Artificial Intelligence or Computer Science with a focus on Artificial Intelligence. The candidate should have an excellent background in algorithms, programming skills in C++ and scripting languages, such as Python, experience and interest in advanced statistical analysis, and a keen interest in automated negotiation and closely related topics in multi-agent systems. Experience with machine learning techniques and tools is required. Experience with automated algorithm configuration techniques would be desirable, but can also be acquired “on the job”. Excellent communication and presentation skills in English are required, as well as the ability to work in a team and a strong commitment to curiosity-driven, basic research.

During the project, you will be embedded into the multi-institutional Hybrid Intelligence Project and, locally, into the ADA Research Group at Leiden University, one of the world’s leading groups on automated machine learning. You will be guided towards and expected to publish original research in top venues in AI, with the goal of earning a PhD degree (dr.) at Leiden University, under the supervision of Profs. Holger Hoos and Catholijn Jonker. You will participate in the education and supervision programme of the Leiden Graduate School of Science, as well as in the Hybrid Intelligence project graduate school. Teaching duties of max 10% of the time are foreseen. You will have opportunities to interact with some of the world’s foremost authorities on the topic of your research and related areas of AI.

To apply for this position click here [link to university job site

Contact: j.m.tomczak@vu.nl

Research Project Description
At Vrije Universiteit Amsterdam, we are looking for an enthusiastic PhD candidate who is interested in formulating and developing new models and algorithms for quantifying uncertainty and making decisions in changing environments. Our project “Continual learning and deep generative modeling for adaptive systems’’ focuses on fundamental research into combining various learning paradigms for building intelligent systems capable of learning in a continuous manner and evaluating uncertainty of the surrounding environment.

Adaptivity is a crucial capability of living organisms. Current machine learning systems are not equipped with tools that allow them to adjust to new situations and understand their surroundings (e.g., observed data). For instance, a robot should be able to adapt to new environment or task and assess whether the observed reality is known (i.e., likely events) or it should contact a human operator due to unusual observations (i.e., high uncertainty). Moreover, we claim that uncertainty assessment is crucial for communicating with human beings and for decision making.

In this project, we aim at designing new models and learning algorithms by combining multiple machine learning methods and developing new ones. In order to quantify uncertainties, we prefer to use deep generative modeling paradigm and frameworks like Variational Autoencoders and flow-based models. However, we believe that standard learning techniques are insufficient to update models and, therefore, continual learning (a.k.a. life-long learning, continuous learning) should be used. Since this is still an open question how continual learning ought to be formulated, we propose to explore different directions that could include, but are not limited to Bayesian nonparametrics and (Bayesian) model distillation. Moreover, a combination of continual learning and deep generative modeling entails new challenges and new research questions.

The PhD candidate will be part of the Department of Computer Science of the Vrije Universiteit Amsterdam (Computational Intelligence group) in a close partnership with the Institute of Informatics of the University of Amsterdam (Amsterdam Machine Learning Lab). Daily supervision will be performed by dr. Jakub Tomczak (VU). The promotor will be prof. A.E. Eiben (VU) and the co-promotor will be prof. M. Welling (UvA).

Job Requirements
The prospective candidate has a Master’s degree or equivalent in AI, Computer Science, Mathematics, Statistics, Data Science or Physics. Candidates from other fields are welcome if they can prove their experience in machine learning/deep learning. Moreover, a candidate should have a solid background in mathematics and programming (Python), demonstrated by successful completion of associated courses, and a keen interest in Artificial Intelligence.

To apply for this position click here [link to university job site

Contact: mark.neerincx@tno.nl

Research Project Description
The ambition for this Phd.D. position will be to define design patterns for successful configurations of Hybrid Intelligent systems. Such patterns describe how different combinations of machine and human capabilities perform for a given task under a given set of circumstances. We aim to develop a corresponding pattern language to express such design patterns in conceptual, (semi-)formal or computational form, and an empirical method to validate the patterns. See for more information.

Job Requirements
A succesful candidate will have an MSc in Artificial Intelligence with an interest in Cognitive Science, or vice versa, or an equivalent other degree with proven affinity to AI and Cognitive Science. The candidate should have skills and interest in modelling human-machine systems and collective or blended human-agent cognition, with an interest in empirically evaluating such models. Good communication and presentation skills in English are required, as well as the ability to work in a team and a strong commitment to research.

See also the detailed project description

To apply for this position click here [link to university job site

Contact: piek.vossen@vu.nl

Research Project Description
To collaborate with AI, such as robots, both people and systems need to understand how they perceive shared situations differently. Understanding these differences is a first step in collaborating successfully. Communication about these situations is fundamental for resolving misunderstanding, explaining perspectives and informing the other. This project addresses the phenomena of identity, reference and perspective within communicative scenarios between AI and people in real-world situations. Knowledge and awareness of the context helps communication between AI and people (successfully identifying and making reference to the world) and through communication we create the common ground for understanding the context (identifying the world from different perspectives). This project tackles these two sides of the same coin by building personal relationships between people and AI in an adaptive environment to learn how to communicate about shared situations or contexts. By building a collective memory of shared encounters, we can define what there is, what is relevant and why we care.

Job Requirements
Who takes up this challenge to make robots talk and develop a personal language that is sensitive to different contexts and user perspectives? We are looking for an enthusiastic and creative PhD candidate that is eager to take up this challenge and make AI systems context aware and truly communicative for collaboration. If you want to take on this challenge and be part of an exciting team working on the forefront of communicating machines, please apply at Vrije Universiteit Amsterdam.

To apply for this position click here [link to university job site

Contact: s.renooij@uu.nl

Research Project Description
At Utrecht University we are looking for an enthusiastic PhD candidate who is interested in raising and educating a new generation of artificially intelligent agents: adaptive agents who need to learn to abide by our rules in hybrid intelligent teams. Our project “Monitoring and constraining adaptive systems” focuses on fundamental research into integrating interpretable knowledge representation and reasoning with learning in the context of adaptive systems.
Since an adaptive system is allowed to change itself, we need to trust that it does not evolve into a system that violates various constraints important for the environment at large in which the system operates. Building upon existing frameworks such as probabilistic graphical models and deep generative modelling, we aim to design a monitoring system that is able to detect and react to violations of constraints, to predict that violations are about to occur, issue warnings, and ultimately gets the adaptive system back on track. The monitoring system should allow for easily capturing constraints in a human-intuitive way, so that they can easily be inspected and changed. Being able to predict the behaviour of an adaptive system also allows for analysing and explaining it, which are important aspects to facilitate communication and collaboration between human and artificial adaptive agents.
The PhD candidate will be part of the Department of Information and Computing Sciences (Intelligent Systems group) in close partnership with the Department of Computer Science of the Vrije Universiteit Amsterdam (Computational Intelligence group). Daily supervision will be performed by dr. Silja Renooij (UU) and dr. Jakub Tomczak (VU).

Job Requirements
The prospective candidate has a Master’s degree or equivalent in Computer Science, Mathematics, (technical) Artificial Intelligence, or related disciplines. She or he has a solid background in maths and programming, demonstrated by successful completion of associated courses, and a keen interest in Artificial Intelligence.

To apply for this position click here [link to university job site

Contact: antske.fokkens@vu.nl

Research Project Description
Natural language processing has a strong tradition in experimental research, where various methods are evaluated on gold standard datasets. Though these experiments can be valuable to determine which methods work best, they do not necessarily provide sufficient insight into the general quality of our methods for real-life applications. There are two questions that often need to be addressed before knowing whether a method is suitable to be used in a real-life application in addition to the outcome of a typical NLP experiment. First, what kind of errors does the method make and how problematic are they for the application? Second, how predictive are results obtained on the benchmark sets for the data that will be used in the real-life application? This project aims to address these two questions combining advanced systematic error analyses and formal comparison of textual data and language models.

Though potential erroneous correlations were still relatively easily identified in scenarios of old-fashioned extensive feature engineering and methods such as K-nearest neighbors, Naive Bayes, logistic regression, SVM, this has become more challenging now that technologies predominantly make use of neural networks. The field has become increasingly interested in exploring ways to interpret neural networks, but, once again, many studies focus on field internal questions (what linguistic information is captured? Which architectures learn compositionality to what extent?). We aim to take this research a step further and see if we can use insights into the workings of deep models to predict how they will work for specific applications that make use of data different from the original evaluation data. Both error analysis and formal comparison methods will contribute to establishing the relation between generic language models, task specific training data, evaluation data and ”new data”. By gaining a more profound understanding of these relations, we try and define metrics that can be used to estimate or even predict to what extent results on a new dataset will be similar to those reported on the evaluation data (both in terms of overall performance and in terms of types of errors).

Job Requirements
The prospective candidate has a Masters degree (MA/MSc) or equivalent in computational linguistics, or related field (AI, Computer Science with focus on NLP). Candidates from other field with a strong background in machine learning can also apply. Solid program skills are required. The project involves interdisciplinary collaboration. The ability to communicate with researchers from different domains is therefore important. Experience with/knowledge of statistical analysis is a plus.

Coming soon: To apply for this position click here [link to university job site]

Contact: h.prakken@uu.nl

Research Project Description
This project aims to explain outcomes of data-driven machine-learning applications that support decision-making procedures to end users of such applications, such as lawyers, business people or ordinary citizens. The techniques should apply in contexts where a human decision maker is informed by data-driven algorithms and where the decisions have ethical, legal or societal implications. They should generate explanations for outputs for specific inputs. The generated explanations should be such that the reasons for the output can be understood and critically examined on their quality. The project will especially focus on explaining ‘black-box’ applications in that it will focus on model-agnostic methods, assuming only access to the training data and the possibility to evaluate a model’s output given input data. This will make the explanation methods independent of a model’s internal structure. This is important since in many real-life applications the learned models will not be interpretable or accessible, for instance, when the model is learned by deep learning or when the application is proprietary.

Job Requirements
candidates with an Msc in AI, computer science, mathematics, data science or a related field, and with a strong interest in interdisciplinary AI research that combines the AI subfields of machine learning, data science, knowledge representation & reasoning and human-computer interaction. Moreover, the candidate should have a commitment to developing computational tools and techniques for helping people make better decisions. The candidate should be able to integrate various research methods and tools, such as formal methods, designing and implementing algorithms and experimental evaluation.

See also the detailed project description

Coming soon: To apply for this position click here [link to university job site]

 

Further positions with 
23.VU prof. Frank van Harmelen (VU), Frank.van.Harmelen@vu.nl
24.UvA prof. Maarten de Rijke (UvA), m.derijke@uva.nl 
25.UvA prof. Max Welling (UvA), m.welling@uva.nl 
26.VU dr. Stefan Schlobach (VU), k.s.schlobach@vu.nl
27.UvA Explainable Machine Learning (UvA)

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