The Centre for Analysis, Scientific Computing and Applications (CASA) of the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) intends to fill a full-time position for a full professor in Machine Learning in Computational Science. General information
The Department of Mathematics and Computer Science provides undergraduate and MSc-programs in Mathematics and Computer Science. The department has research collaborations with other departments at TU/e as well as with a large number of other universities and companies, both nationally and internationally. The department has approximately 400 employees and about 1400 students. Student numbers of the Department of Mathematics and Computer Science grow.CASA is chaired by Mark Peletier (Applied Analysis) and Barry Koren (Scientific Computing) and further consists of one fulltime full professor in Mathematical Image Analysis (Luc Florack), three part-time full professors, four associate professors (three full-time, one part-time), several assistant professors, lecturers and postdocs, and about 30 (internal and external) PhD-students.CASA provides undergraduate and graduate courses in applied analysis and scientific computing, as well as in general mathematics, for all TU/e students. CASA plays a major role in the MSc-program Industrial and Applied Mathematics. The emphasis in the research of CASA’s Scientific Computing Group is on the development and analysis of new numerical methods for societally (mostly industrially) and scientifically relevant problems.CASA plays an active role in national and international computational science activities and participates in three national research schools: the mathematics research school WONDER (being brought under the umbrella of Mastermath), and the engineering schools J.M. Burgerscentrum and Engineering Mechanics.Research area: Machine Learning in Computational Science
Computational Science is of vital importance to today’s and tomorrow’s society. It enables the simulation of processes, phenomena and systems that cannot be studied by real experiments, because these are too dangerous, too expensive, unethical, or just technically impossible. Moreover, as opposed to experiments, Computational Science allows for automatic design and optimization. Every major discipline in science and engineering has its own computational branch. Engineers, medical doctors, policy makers, etc. rely more and more on Computational Science for decision support. With the longstanding, continuing growth in speed, memory and cost-effectiveness of computers, and with similar improvements in numerical algorithms, the existing and future benefits of Computational Science are enormous.In a way that is both similar and completely different, Artificial Intelligence and its engineering counterpart Machine Learning already are standard tools in online retailing, and are set to become similarly standard tools in science, engineering, health care, business, government and more. This success stems from the ability of machine-learning algorithms, and especially neural networks, to capture (`learn’) very complex relationships from data alone, with little guidance from human intelligence.Since many relationships in Computational Science are both complex and unknown, and because of the immense growth in availability of data, it is natural to seek applications of Machine Learning in Computational Science. Some examples of successful use of Machine Learning in Computational Science indeed exist, such as recognition of crystal structures by neural networks, replacement of parameterized constitutive laws by data-driven closure relations, and parametrization of quantum states. Also within CASA examples have been and are being studied of neural-network technology as components of Computational-Science setups.However, the lack of a comprehensive theory of deep learning implies that neural-network applications come without any guarantee of quality or suitability, and the size and type of errors that they introduce are completely unknown. In terms of underlying theory, one could say that machine-learning tools are lagging behind by half a century with respect to the more traditional tools of Computational Science; at the same time, their potential to revolutionize the field seems obvious.The aim of the new chair Machine Learning in Computational Science
is to tackle this mismatch head-on, and develop machine-learning tools in the context of Computational Science, hand-in-hand with the theory for a rational and trustworthy use.
Profile and tasks
The full professor will contribute to the departmental research theme Computational Science and the section CASA. The candidate will take part in the teaching activities of the department, as described above, and will establish/strengthen contacts with other departments both at TU/e and elsewhere.
- To develop research on the use of Machine Learning in Computational Science within the section CASA;
- to develop and manage the new chair Machine Learning in Computational Science;
- to develop and strengthen links with other researchers and research programs in this area, in the Netherlands, and abroad;
- to develop, give and coordinate courses in applied mathematics, and to be responsible for updating these courses;
- to supervise BSc, MSc, and PhD students;
- to initiate, acquire and coordinate research projects with external funding;
- to perform managerial and/or administrative tasks for the department.
The candidate should have a deep and broad insight in the research domain Computational Science, and have provable affinity for and experience with Machine Learning. Besides, the candidate should fulfil the usual demands for a full professor (as described in: Appointment Policy for Academic Staff in the Department of Mathematics and Computer Science), more specifically:
- A PhD degree related to the described research area;
- ample experience in scientific research, as reflected in internationally refereed scientific publications;
- international recognition by experts in the field, as reflected e.g. by invited lectures at major conferences or renowned institutes;
- experience in (content-wise) coaching research work of others;
- experience in initiation of research projects and acquisition of the necessary funding;
- active memberships or positions in relevant academic associations;
- working experience at one or more reputable universities or research institutes abroad, as a guest researcher or visiting scholar
- Teaching experience on an academic level, preferably in a core position teaching the particular field of study;
- proven didactic qualifications and inspiring personality as a teacher;
- experience in developing didactic components.
- Demonstrable management qualities.
- The ability to act as an advisor on issues related to industry.
- Evidence of preparedness and suitability to work in a team, and as a team manager;
- evidence of having taken initiatives, and being capable and willing to do so;
- motivating and inspiring personality.
- A challenging job in a dynamic and ambitious university with the autonomy to develop your own research line and participate in the curriculum of the department.
- As an Irène Curie Fellow, you are entitled to a substantial start-up package to kick-off your career.
- In order to empower you, we provide support such as training programs for academic leadership and the university teaching qualification and a dedicated mentoring program to help you get to know the university and the Dutch (research) environment
- Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
- Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary;
- A broad package of fringe benefits (including an excellent technical infrastructure, moving expenses, savings schemes).
Family friendly initiatives are in place, such as the Dual Career Opportunity program to support accompanying partners, an international spouse program, and excellent on-campus children day care and sports facilitie
38 hours per week
De Rondom 70