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Job description We are expanding our team of Business Information Management faculty members to complement our ongoing research into this increasingly …
The Faculty of Science and Leiden Institute for Advanced Computer Science is looking for a PhD candidate in nonlinear optimization, data science and machine learning (3 positions, 1.0 FTE).
We are looking for PhD candidates for a Marie-Sklodowska Curie Innovative Training Network named ECOLE: Experience-Based Computation – Learning to Optimize. This unique training programme is initiated by a consortium of academic and industrial partners consisting of the University of Birmingham (United Kingdom), Leiden University (Netherlands), the Honda Research Institute (Offenbach, Germany), and NEC Laboratories Europe (Heidelberg, Germany).
Within the programme research will be performed to seek novel synergies between nature inspired optimization (e.g., evolutionary computation) and machine learning to address new challenges that arise in industry due to the increasing complexity of products, product development and production processes. The unique aspect of ECOLE is to study and capture the notion of experience that is associated with expert engineers, who have worked on complex optimization tasks for a certain time, in a computational framework composed of machine learning and optimization strategies. We aim at developing cutting-edge optimization algorithms that can continuously accumulate experience by learning from development projects both over time and across different problem categories. The more such algorithms are used for different optimization problems, the better they become since their accumulated experience increases.
The PhD candidates will spend 50% of their time in the non-academic beneficiaries and be trained in different academic environments and industrial sectors.
We offer a one year term position with the possibility of three-year renewal after a positive evaluation. Salary range from €2,222 to €2,840 gross per month (pay scale P, in accordance with the Collective Labour Agreement for Dutch Universities).
Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break. More at https://www.universiteitleiden.nl/en/working-at/job-application-procedure-and-employment-conditions.
All our PhD students are embedded in the Leiden University Graduate School of Science www.graduateschools.leidenuniv.nl . Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness. In addition, advanced courses to deepen scientific knowledge are offered by the research school.
Mobility rule for MSCA-ITN programme: researchers must not have resided or carried out their main activity (work, studies, etc.) in the Netherlands for more than 12 months in the 3 years immediately before the recruitment date.
Leiden University is strongly committed to diversity within its community and especially welcomes applications from members of underrepresented groups.
Enquiries or any questions about the procedure can be made to Prof. Thomas Bäck, email email@example.com
To apply for this vacancy, please send no later than 1 May 2018 an email to Marloes van der Nat at firstname.lastname@example.org.
Please ensure that you submit the following additional documents quoting the vacancy number:
Leiden is a typical university city, hosting the oldest university in the Netherlands (1575). The University permeates the local surroundings; University premises are scattered throughout the city, and the students who live and study in Leiden give the city its relaxed yet vibrant atmosphere.
Leiden University is one of Europe's foremost research universities. This prominent position gives our graduates a leading edge in applying for academic posts and for functions outside academia. More at https://www.universiteitleiden.nl/en/working-at.Faculty of Science
The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are driven by curiosity to expand fundamental knowledge and to look beyond the borders of their own discipline; their aim is to benefit science, and to make a contribution to addressing the major societal challenges of the future.
The research carried out at the Faculty of Science is very diverse, ranging from mathematics, computer science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organized in eight institutes. These institutes offer eight bachelor’s and twelve master’s programmes. The faculty has grown strongly in recent years and now has more than 1,300 staff and almost 4,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see www.universiteitleiden.nl/en/science.
The Leiden Institute of Advanced Computer Science (LIACS) is the Computer Science Institute in the Faculty of Science of Leiden University. According to our recent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. For more information about LIACS, see https://www.cs.leiden.edu/https://www.universiteitleiden.nl/en/science/computer-science.
ECOLE fills an urgent need in Europe for highly skilled optimization and machine learning experts who have first-hand industrial experience allowing sustainable know-how growth for solving future challenges. Its entire training programme is centered around a set of novel research projects proposed for solving future challenges, complemented by domain knowledge training, hand-on engineering training and transferable skill training.