Are you interested in deep reinforcement learning (RL)? Would you like to expand its limits to biomedical applications, like neuroscience? Find theoretical solutions for practical problems? Think outside the box of what is currently possible? Create cutting edge artificial neural network architectures that can interact with biological neurons?

Imagine an implant needs to interact with the brain, e.g., to stop an epileptic seizure or to control prosthetics. Deep RL has been at the core of many recent success stories in AI, but is restricted to pattern recognition, and no two brains are the same. We want to develop AI that generalizes over individual characteristics and adapts where generalization is impossible. Developed algorithms will allow control of small systems of neurons, first in simulation and eventually in vitro. This pushes the limits of RL as we understand it today and will have applications far beyond neuroscience. Come, help us move the goalpost!

During your PhD you will develop new network architectures and training algorithms that allow quick adaptation to unseen configurations of input images. You will train these on classical benchmarks, like computer games, and on novel neuroscience applications, like controlling optogenetically modified neurons with directed laser pulses. The challenge is to implicitly identify where neurons are in the image, how they are connected and how they will  respond to stimuli. Without hand-labelling by an expert, who often cannot identify the neural connections from the image, and with use of minimal training data, this has to be learned end-to-end by observing the reactions to the AI’s actions.

You will work closely with other PhD students in the Algorithmics group that investigate generalization in RL, and with students from the Brinks Lab who simulate neural systems and work with lab-grown cultures under microscopes. The PhD position is focused on theoretical RL, with modulation and control of biological neural networks as the main benchmark application. The work can therefore be broadened to include biological experimentation as well if the student wants to.

This one of four PhD postions within the BIOLAB, for a complete overview check: https://www.tudelft.nl/ai/biolab

In the Biomedical Intervention Optimization lab (BIOlab), experts in computer vision (Tömen), reinforcement learning (Böhmer), neural architecture (Brinks), deep learning and computational physics (Perkó), and biomedical imaging (Gruβmayer, Carroll) join forces to create high-efficiency, real-time, AI-driven feedback and control in biomedical applications.

To qualify for this position you must have:

• completed a relevant MSc degree in artificial intelligence, computer science, computational neuroscience or an applied sciences field related to PhD research
• demonstrated competences in one or more of these categories: reinforcement learning, machine learning, computational neuroscience, or relevant appled sciences
• demonstrated experience in programming in at least one of the following programming languages: Python, C/C++
• a proven record and interest in further developing your modelling, programming and analytical skills
• strong affinity and enthusiasm for state-of-the-art reinforcement learning and AI approaches
• excellent critical and analytical thinking skills
• An affinity with teaching and guiding students
• Proficiency in expressing yourself verbally and in writing in English
• The ability to work in a team and take initiative

In addition:

• strong knowledge of Python and knowledge of or motivation to work with popular deep learning libraries (Tensorflow, PyTorch) is a plus
• background knowledge in computational sciences (computational physics, biology, chemistry) or nonlinear dynamics is a plus
• background or interest in computational neuroscience is a plus

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

You will receive a 5-year contract and will be deployed for AI-related education for the usual teaching effort for PhD candidates in the faculty plus an additional 20%. The extra year compared to the usual 4-year contract accommodates the 20% additional AI, Data and Digitalisation education related activities. All team members have many opportunities for self-development. You will be a member of the thriving TU Delft AI Lab community that fosters cross-fertilization between talents with different expertise and disciplines.

You will be offered a 5-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 3,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2443 per month in the first year to € 3122 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact! 

Faculty Electrical Engineering, Mathematics and Computer Science

This position is connected to the Biomedical Intervention Optimization lab (BIOlab). BIOLab is a new TU Delft Artificial Intelligence Lab. Artificial Intelligence, Data and Digitalisation are becoming increasingly important when looking for answers to major scientific and societal challenges. In a TU Delft AI Lab, experts in ‘the fundamentals of AI technology’ along with experts in ‘AI challenges’ run a shared lab.

As a PhD, you will work with at least two academic members of staff and three other PhD candidates. In total TU Delft will establish 24 TU Delft AI Labs, where 48 Tenure Trackers and 96 PhD candidates will have the opportunity to push the boundaries of science using AI. Each team is driven by research questions which arise from scientific and societal challenges, and contribute to the development and execution of domain specific education.

Goal of the Biomedical Intervention Optimization lab (BIOlab)

Modern machine learning algorithms have achieved unprecedented accuracy in image and video understanding tasks by purely learning from data. These powerful abilities come at the price of enormous amounts of training data, memory and computational requirements. However, those resources are rarely available to real-time feedback systems in medical intervention and biomedical research.

The lab will focus on improving the efficiency of machine learning algorithms by designing novel artificial neural network architectures, developing new reinforcement learning and generative algorithms, and incorporating biologically-inspired neural network models. These newly developed concepts and algorithms will be applied to a wide range of problems in biomedical applications, such as optimizing tumor irradiation protocols with missing information, and in smart (super-resolution) microscopy to limit irradiation damage to delicate living samples.

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three disciplines - electrical engineering, mathematics and computer science. Combined, they reinforce each other and are the driving force behind the technology we use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make future-proof. We are also working on a world in which humans and computers reinforce each other. We are mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. There is plenty of room here for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1,100 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.