[Statlist] PhD position (statistical data science)

[David Ginsbourger]

Dear colleagues,

Please find (this time) below an advertisement for a PhD position in my 
group.

Best regards,

David

*_PhD position (statistical data science)_*

A PhD student position is open in Prof. Dr. David Ginsbourger’s 
uncertainty quantification and spatial statistics group 
<https://www.imsv.unibe.ch/research/research_groups/uncertainty_quantification_and_spatial_statistics/index_eng.html>at 
the Institute of Mathematical Statistics and Actuarial Science, 
University of Bern, Switzerland.

*Context:*The recruited PhD student will be supervised by Prof. Dr. 
David Ginsbourger and affiliated with his group.  The group is involved 
in collaborations with national and international partners from various 
research fields. These include geosciences, with a history of 
collaborations around stochastic modelling in hydrogeology, geophysical 
inversion, and atmospheric sciences (with links to the Oeschger Center 
of Climate Change Research). Recently, the group has also been 
increasingly collaborating with the Institute of Social and Preventive 
Medicine and the University Hospital around statistical machine learning 
for biomedical applications.

*Project:*The recruited PhD student in statistics will work on the 
sub-project “Questioning similarities for expert-informed statistical 
learning” part of the collaborative project “Perception in Statistics 
and Econometrics” funded by the University of Bern.

Distance- and similarity-based methods (nearest-neighbour 
classification, phylogenetic trees, multi-dimensional scaling, etc.) are 
at the heart of many predictive approaches in statistical data science 
and machine learning. Yet, distance/similarity functions are often 
chosen off the shelf, without necessarily questioning their adequacy to 
the considered task. For instance, the Euclidean and Gower distances 
typically appear as default choices when dealing with real-valued and 
mixed continous-categorical covariables, respectively. While metric 
learning has arose in recent years as an automatic method to tune 
distances in distance-based machine learning, a number of issues remain 
open when it comes to diagnosing the suitability of prescribed and tuned 
distance functions to tasks of interests. In this thesis, we will 
pioneer the field with novel diagnostics tools and algorithms extending 
approaches from spatial statistics and beyond (including variography, 
cross-validation, and more) to efficiently choose from and combine 
expert-informed distance functions towards improved predictivity and 
uncertainty quantification.

*Sought profile:*The ideal candidate will have recently earned or be 
about to finish their Master’s degree in statistics or neighbouring 
subjects with a strong mathematical component, a genuine interest in 
statistical data science and applications thereof, a taste for both 
theoretical investigations and numerical experiments, and solid 
programming skills.The salary will be at the level foreseen by the 
University of Bern. There might be a possibility to be involved in 
teaching and/or consulting duties. The funding is secured for up to 48 
months with the starting date of July 1st 2023 or as can be arranged by 
mutual agreement.

*Applications should contain*: (1) a letter in which the applicants 
describe their research interests and motivations, (2) a complete CV, 
(3) copies of relevant diplomas, certificates and transcripts of 
records, (4) an electronic version of a research work (Master’s thesis 
or other scientific publication), (5) contact information of 2 – 3 
references.

Applications and inquiries should be sent to Prof. Dr. David Ginsbourger 
(respectively via this link 
<https://ohws.prospective.ch/public/v1/jobs/faf3885f-c92d-4d68-bf4b-2378edb18a72>and 
via e-mail to david.ginsbourger using unibe.ch 
<mailto:david.ginsbourger using unibe.ch>)

https://ohws.prospective.ch/public/v1/jobs/faf3885f-c92d-4d68-bf4b-2378edb18a72

-- 
Prof. Dr. David Ginsbourger
Director of Studies in Statistics
Institute of Mathematical Statistics and Actuarial Science
University of Bern

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