PhD Position: Closed-Loop Optimization of Experimental Parameters for Neurotechnological Systems

Are you passionate about leveraging optimisation methods and machine learning techniques to enhance neurotechnological systems such as brain-computer interfaces? Do you have a solid foundation in mathematics, optimisation, machine learning and programming? If so, you are invited to become part of the Dutch national brain interfaces initiative (DBI2).

We invite applications for a PhD position to investigate sample-efficient optimisation strategies for experimental parameters. The position is to be filled as soon as possible.

Neurotechnological systems such as brain-computer interfaces (BCIs) allow us to record and interpret the brain activity of healthy users, patients or animal models in real time. Thus, BCIs not only allow us to study fundamental brain functions but they also provide applications for communication, for the control of devices, or to support the treatment of neurological or psychiatric diseases. As brain signals are individual, noisy and high dimensional, machine learning methods play a crucial role in extracting information about the ongoing brain state.

Parameters of an experimental protocol can strongly influence the measured brain signals, but parameters that are suitable for one participant may not be for another. This calls for individually optimised protocol parameters. Ideally, individual best parameters are determined in a closed-loop approach during a single experimental session. As the measured EEG / MEG / LFP / sEEG / ECoG signals are very noisy, either only a small number of parameter sets can be evaluated within one session, or each parameter set needs to be rated based on a very small amount of brain signals which, of course, may deliver noisy ratings.

The PhD project investigates optimisation approaches for parameters of neurotechnological applications with the goal to cope with noisy objective functions. The focus will be on how (1) experimental protocol parameters and (2) machine learning methods for the decoding of brain signals can be co-optimised. For both tasks, domain-specific regularisation approaches shall be explored.

As a PhD candidate, you will investigate novel optimisation strategies in simulations before translating them into experiments with a human participant in the loop. You will be expected to design and implement experimental protocols in Python. You will conduct non-invasive and invasive closed-loop experiments in our own EEG labs, in labs of our DBI2 partners or clinics, and train machine learning models to analyse our own data and the data of our scientific partners. You will help disseminate the results in high-impact papers and scientific journals, and at conferences and workshops.

This is a fixed-term (4 year), full-time position. You will be expected to participate in teaching activities involving Bachelor’s and Master’s degree students, which will take 10% of your working time. Throughout the project, you will receive guidance from Dr Michael Tangermann and be an integral part of the Data-Driven Neurotechnology Lab. The lab is situated within the Machine Learning and Neural Computing department and embedded in the Donders Institute.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

• We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract).
• You will receive a starting salary of €2,901 gross per month based on a 38-hour working week, which will increase to €3,707 in the fourth year (salary scale P).
• You will receive an 8% holiday allowance and an 8,3% end-of-year bonus.
• We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands.
• You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.

Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.

You will be joining the Data-Driven Neurotechnology Lab which is embedded in the Donders Institute and in the Department of Machine Learning and Neural Computing of the Radboud University. We are ten scientists at different academic levels, with backgrounds in Computer Science, Biology, Biomedical Engineering, Artificial Intelligence, Physics, and Cognitive Neuroscience. With our lab members having lived in 9 different countries, we celebrate and embrace cultural richness.

The lab pushes the boundaries of neurotechnology by interacting with the central nervous system. We contribute to the field of brain-computer interfaces, adaptive deep-brain stimulation and stroke rehabilitation by novel artificial intelligence methods, that allow us to decode brain states in real-time and to deliver matching stimuli that beneficially modulate brain activity. Our multidisciplinary research requires collaborations with clinicians, patients, patient organizations, ethics committees and companies.

Your position is funded by the Dutch Brain Interface Initiative (DBI2), a consortium project enabled by NWO's Gravitation programme of the Dutch government. DBI2 aims to advance our understanding of brain function and brain-environment interactions. It brings together academics from various universities and research institutes in the Netherlands, organizes retreats and training weeks for young academics, and fosters collaboration and skill development.

The Donders Institute for Brain, Cognition and Behaviour is a world-class interfaculty research centre, hosting state-of-the-art research facilities for its more than 700 researchers. English is the lingua franca at the Institute. You will be part of the Donders Graduate School, a PhD program embedded into the Donders Institute of the Radboud University. Our lab’s embedding in the Donders Institute offers various opportunities for collaborations. The PhD candidate will also benefit from the extensive training programme of the Donders Graduate School, and from the interaction with academic and industrial partners of the DBI2 network.

You can apply only via the button below. Address your letter of application to Michael Tangermann. Please provide the following documents with your application:

• Motivation letter that clearly explains your fit to this PhD topic
• CV, including a publication list, a list of your own software repositories/projects, the contact details of two references, and your earliest possible availability
• Course transcripts containing the full list of courses, credits and grades of both your BSc and MSc degrees (as well as their translation to English). If you haven’t finished your degree yet, please provide the partial list.
• Proof of English proficiency (TOEFL, IELTS, CAE, TELC, …) if available

The first interviews will take place on Friday 25 April. Any second interview will take place on Friday 2 May. You will preferably start your employment on 1 June 2025.

We can imagine you're curious about our application procedure. It describes what you can expect during the application procedure and how we handle your personal data and internal and external candidates.