[DMANET] PhDs and PostDoc positions in the Netherlands

5 PhD and 6 Postdoc Positions available in the Netherlands.

Optimization for and with Machine Learning (OPTIMAL) OPTIMAL is a
Dutch ENW-groot project funded by NWO (2019-2025), offering 5 PhD
and 6 PostDoc positions in the Netherlands, starting from September
2020 or later.

Short description of the research project:
A key component of machine learning is mathematical optimization,
that is used, for example, to train neural networks. The goal of
this project is to provide new analysis and tools for optimization
problems and algorithms arising in machine learning, but also to use
insights and tools from machine learning to improve optimization
methods. This explains the project title 'Optimization for and with
machine learning'. The project consists of four connected
workpackages. The first two workpackages are related to
'optimization for machine learning'. In the first workpackage we
will investigate why the optimization methods currently used in
machine learning are often successful in practice and analyze
the limits of their computational tractability. The second
workpackage is aimed at enhancing the existing optimization
algorithms and developing new ones to obtain more accurate machine
learning models in an efficient way. The last two workpackages are
related to 'optimization with machine learning'. The third
workpackage is aimed at using machine learning to obtain
data-centric approximation and optimization algorithms. We will
develop algorithms that adapt to the specific data characteristics
of the problem instance. The advantage of such data-centric
algorithms is more accurate solutions and/or less computation time.
In the fourth workpackage we will develop a data-centric
optimization modeling approach. In such an approach parts of the
resulting optimization model are obtained via machine learning. This
data-centric modeling can be used to get more accurate models or can
be used in cases where there is no theoretical knowledge available
to build the model manually. In addition, we will test our insights
on a variety of applications where the consortium members are
already involved, including classification problems in the medical
sciences, decision problems related to the UN World Food Programme,
and routing of shared, self-driving cars.

The institutes and researchers involved in OPTIMAL:
Tilburg University, Tilburg (Dick den Hertog, Etienne de Klerk)
CWI, Amsterdam (Nikhil Bansal, Monique Laurent, Leen Stougie)
Delft University of Technology (Karen Aardal, Leo van Iersel).

List of workpackages WP1:
Performance analysis of optimization methods for machine learning.
The optimization model used to train the machine learning model has
mathematical properties that could make it hard to solve. However,
in practice it can often be solved efficiently with rather simple
optimization algorithms. This is an enigmatic feature of machine
learning: it often works, but it is not well understood why it
works. The goal of this workpackage is to understand the success of
these simple optimization methods, and to analyze the limits of its
computational tractability. WP2: New optimization methods for
machine learning. In this workpackage we will develop new
optimization methods for obtaining more accurate machine
learning models in a more efficient way. The main objectives
in this workpackage will be to investigate and exploit structural
properties of data, which can be geometric, algebraic or
combinatorial, for the design of dedicated solution approaches. In
particular we will investigate the use of polynomial functions in
machine learning. The resulting training problem is a so-called
polynomial optimization problem that has been extensively studied in
the optimization field in recent years. Moreover, we will focus on
better optimization methods for classification trees. WP3:
Data-centric algorithm design. When we are faced with an
optimization problem there are two main alternatives for finding
solutions. We can either develop an optimization algorithm for
finding a provably best solution, or we can settle for a
high-quality solution that is obtained "fast" through an
approximation algorithm. For optimization algorithms we will
investigate how machine learning can be used to guide the search to
an optimal solution. When approximating, it is a challenge to derive
algorithms that are not only guaranteed to perform well in the
worst-case sense, as is mainly done today, but more interestingly
for data that actually occur. We will develop new theoretical
concepts for a beyond-worst-case analysis that incorporates data. In
addition, we will develop algorithms that have a guaranteed
performance according to the developed theory. WP4: Data-centric
modeling. Traditionally, an optimization model is built manually,
but this is not always possible as some restrictions do not easily
"translate" into mathematical functions. We will investigate how to
use machine learning to generate a (part of the) model for a given
problem based on the available data. Hence, this results in
optimization models that contain, e.g., a deep learning model or a
random forest. The machine learning model is added as a constraint
to the manually developed model. In this workpackage we will analyze
which machine learning techniques can best be used, and how to solve
optimization models that also contain nonlinear functions that
result from machine learning.

Submission Guidelines:
To apply for one or more of these PhD or PostDoc positions, the
applicant can find more information on the following websites or can
request for more information by e-mail:

PhD (WP2) and PD (WP3) position of Nikhil Bansal:
https://www.win.tue.nl/~nikhil/Vacancies.html

PhD (WP4) and PD (WP4) position of Dick den Hertog:
D.denHertog@tilburguniversity.edu

PhD (WP1) and PD (WP1) position of Etienne de Klerk:
https://sites.google.com/site/homepageetiennedeklerk/postdoc-phd-positions-available

PD (WP2) position of Monique Laurent:
https://homepages.cwi.nl/~monique/optimal/

PhD (WP3) and PD (WP2) position of Leen Stougie / Leo van Iersel:
https://leovaniersel.wordpress.com/positions/

Between brackets we have indicated to which workpackage the PhD or
Postdoc position is related.

The deadline for submission is June 7, 2020. (Exception: deadline
for PhD and PD positions of Leen Stougie / Leo van Iersel: June 30,
2020.) However, the positions may be reopened later.

Contact Questions about submissions or workpackages should be
emailed to the corresponding OPTIMAL researcher mentioned above.
General questions on OPTIMAL should be emailed to the project leader
of OPTIMAL: Dick den Hertog (D.denHertog@tilburguniversity.edu).
**********************************************************
*
* Contributions to be spread via DMANET are submitted to
*
* DMANET@zpr.uni-koeln.de
*
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.
*
* DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET)
* http://www.zaik.uni-koeln.de/AFS/publications/dmanet/
*
**********************************************************