PhD Candidate or Postdoctoral Researcher in Quantum Machine Learning (1.0 FTE)
PhD Candidate or Postdoctoral Researcher in Quantum Machine Learning
Employment: 1.0 FTE
Gross monthly salary: € 2,434 - € 4,474
Faculty of Science
Required background: Research University Degree
Application deadline: 15 September 2021
We are looking for
Are you intrigued by Machine Learning? As a PhD Candidate/Postdoctoral Researcher you will conduct independent research on the application of quantum formalism to develop novel algorithms for machine learning and/or to find new computational models for information processing in the brain. You have the freedom to develop your own research ideas and will be working in a stimulating environment for top researchers and young talent.
An example of previous work in this direction is the quantum Boltzmann machine that describes the learning of density matrices. Another direction is the atomic Boltzmann machine, where the dynamics of individual atoms may allow for an effective quantum description. Examples of possible research topics are efficient simulation of quantum systems through variational inference, learning quantum neural networks with quantum variational circuits, quantum reservoir computing or open neural quantum systems. You will develop your own research ideas in collaboration with Prof. Bert Kappen. Your work should lead to a number of publications in international journals and you will present your findings at international conferences.
A Master's or PhD degree in Physics with a strong theoretical background (in either Statistical Physics or Quantum Physics).
A broad interest in Machine Learning.
Keenness to engage in exploratory research.
A good command of English.
Highly motivated, enthusiastic, critical and creative.
As a PhD candidate or postdoctoral researcher in Quantum Machine Learning you will be embedded in the Physics of Machine Learning and Complex Systems group at the Donders Centre for Neuroscience. The group is led by Bert Kappen and Ton Coolen and conducts research on the interface of theoretical physics, machine learning, neuroscience, data science, quantum computing and medicine. The group coordinates the European Ellis programme on quantum and physics based Machine Learning.
The group is embedded in the Donders Centre for Neuroscience (DCN), which is part of the Donders Institute for Brain, Cognition and Behaviour at the Faculty of Science of Radboud University (Nijmegen, Netherlands). The Donders Institute hosts 800 researchers and conducts world class research on a broad range of neuroscience topics, as well as machine learning and artificial intelligence research. The Donders Institute has been assessed by an international evaluation committee as 'excellent' and recognised as a 'very stimulating environment for top researchers, as well as for young talent'. It fosters a collaborative, multidisciplinary, supportive research environment with a diverse international staff.
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 22,000 students and 5,000 employees at Radboud University. And this requires even more talent, collaboration and lifelong learning. You have a part to play!
Employment: 1.0 FTE.
The position can be filled at either PhD level or Postdoctoral level.
The gross starting salary for a PhD candidate amounts to €2,434 per month based on a 38-hour working week, and will increase to €3,111 in the fourth year (salary scale P). You will be appointed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract).
The gross monthly salary for a postdoctoral researcher amounts to a minimum of € 2,836 and a maximum of € 4,474 based on a 38-hour working week, depending on previous education and number of years of relevant work experience (salary scale 10). You will be appointed for an initial period of 12 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2 years (3 year contract).
In addition to the salary: an 8% holiday allowance and an 8.3% end-of-year bonus.
As a PhD candidate, your teaching load may be up to 10% of your total FTE.
The intended start date is preferably before 1 January 2022.
You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
Additional employment conditions
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.
Would you like more information?
For more information about this vacancy, please contact:
Bert Kappen, Professor of Machine Learning and Neural Networks
Tel.: +31 6 52 07 82 10
Please address your application to Prof. Bert Kappen and submit it, no later than 15 September 2021, 23:59 Amsterdam Time Zone.
Your application should include the following attachments:
Letter of motivation.
The first round of interviews will take place in the second half of September.
We drafted this vacancy to find and hire our new colleague ourselves. Recruitment agencies are kindly requested to refrain from responding.