Connectionists: Two PhD Positions in Machine Learning/Neuroimaging at Charité Berlin

[Stefan Haufe]

Dear all,

The Brain and Data Science Lab of Stefan Haufe at Charité -
Universitätsmedizin Berlin is looking to recruit two highly motivated
PhD students. Appointments can be made for four years starting immediately.

1. Theory and practice of interpreting machine learning models

Machine learning systems have recently set new standards for solving a
wide range of problems by leveraging vast amounts of training data. But
they have remained “black boxes” whose internal workings are too complex
to be comprehensible by a human. Especially in the health domain, it is
desirable to explain and visualize the decisions of ML models. Recently,
it has been shown that many existing explanation methods can be
misleading even when using simple linear ML models [Haufe et al., 2014;
Kindermans et al., 2018]. In this PhD project, a bottom-up approach will
be performed, in which correct interpretation will be defined
axiomatically. The resulting definition will be used to benchmark novel
and existing explanation methods using synthetic ground-truth data. The
resulting methodology will also be applied to clinical use cases.


2. Estimating and characterizing EEG/MEG functional connectomes in aging
and dementia

The study of functional brain interactions promises to greatly enhance
our understanding of mental diseases. EEG and MEG make it possible to
brain dynamics at high temporal scales but suffer from low spatial
resolution, which often leads to false detections of brain connectivity.
While the problem has been overcome for linear connectivity metrics
[Nolte et al., 2004; Haufe et al., 2013] it still persists for
non-linear interactions such as phase-amplitude and amplitude-amplitude
coupling, which have been postulated as possible mechanisms of brain
communication. This PhD project will establish a best practice to
reconstruct non-linear connectivity from EEG/MEG data. The developed
pipeline will be applied to study brain connectivity in aging and
neurological conditions (dementia) using big EEG/MEG datasets.
Interpretable machine learning will further be used to relate functional
connectomes to behavior and relevant clinical variables.

The Brain and Data Science Group at Charité develops machine learning
and signal processing methods for the analysis of non-invasive brain
signals in health and disease. We are located at the Berlin Center for
Advanced Neuroimaging (BCAN) on the beautiful historic Charité Campus
Mitte in the center of Berlin, and are embedded in a stimulating
inter-disciplinary research environment. The group is funded by an ERC
starting grant of the European Union.

Requirements: Candidates are expected to hold a very good MSc or
equivalent degree preferably in a technical field (machine learning,
computer science, statistics, mathematics, computational (neuro)
science, data science, physics, electrical/biomedical engineering,
etc.). All positions require a solid math/statistics background,
proficiency in written English, and good coding skills (e.g., Matlab,
Python, C++, Java). Prior experience with functional neuroimaging data
is a plus. Applications should include a letter of motivation, a CV,
transcripts and degree certificates, as well as (if available)
references, an English-language writing sample, and a coding sample
(e.g. link to a github project). Applications should be sent to
stefan.haufe at charite.de until Oct 15th 2019. All attached documents
should be contained in a single pdf.

See braindata.charite.de for further information on the positions and
our group.

With best wishes,
Stefan Haufe

-- 
Stefan Haufe
ERC Research Group Leader

Charité - Universitätsmedizin Berlin
Berlin Center for Advanced Neuroimaging (BCAN)
Charitéplatz 1
Sauerbruchweg 4
10117 Berlin

Tel: +49 30 450 639 639
Fax: +49 30 450 539 951
Web: braindata.charite.de

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