Recent International Research Positions for Machine Learning

Machine learning is believed to be the most upcoming era in near future. Many interdisciplinary branches are moving towards machine learning for data analysis. The data, being huge and big, is analyzed automatically, using many machine learning based algorithms for clustering, classification and much more. There are huge number of doctorate, post doctorate and other research positions which are out recently in international market. We have enlisted different opportunities in this blog. Although, the formal procedure to apply for post doctoral position is out of scope of this article and I may define about this in near future.

The machine learning positions are:

Multiple Senior Research Positions for Team Leaders at RISE, Cyprus

The Research Center on Interactive Media, Smart Systems, and Emerging Technologieshas openings for several positions of Team Leaders for the creation of Multidisciplinary Research Groups within the center.

The full announcement, deadlines, and application procedure can be found here: http://www.rise.org.cy/en-gb/vacancies/team-leaders/

3 PhD Positions on AI-networking at Delft University of Technology

The ability to accurately discover all hidden relations between items that share similarities is paramount to solving large optimization problems that pertain to artificial intelligence and networking. By embedding our recently developed clustering techniques into reinforcement learning problems, we will optimize several processes within KPN that relate to learning, decision taking, recommendation giving, and prediction making.

Background: probability theory, stochastic processes, network science, decision theory

Click Here for more details

Postdoc in machine learning or econometrics – University of Pennsylvania

Postdoc with background in machine learning or econometrics sought for an interdisciplinary research project developing and testing machine learning methods for inferring causality, including constructing statistically efficient methods for dynamically assigning subjects to “A/B testing” trials with dozens of treatment arms and developing novel methods to optimally exploit population heterogeneity matching treatments to people.

The position is at the University of Pennsylvania, but you will work with a distinguished team of economists and computer scientists including
Katy Milkman (Wharton, U. Pennsylvania)
Sendhil Mullainathan (Chicago Booth)
Jann Spiess (MSR New England; Stanford GSB (effective July 2019))
Lyle Ungar (CIS U. Pennsylvania)

Open positions at LMU Munich in NLP and Deep Learning

PHD 1: NEURAL ARCHITECTURES FOR REPRESENTATION LEARNING

This PhD candidate, funded by the European Research Council
Advanced Grant NonSequeToR, will work on developing neural
architectures for learning the next generation of
representations for NLP. Topics include contextualized
representations, multitask learning and robust cross-lingual
methods. (PhD advisor: Hinrich Schuetze)

PHD 2: MUNICH CENTER FOR MACHINE LEARNING (MCML)

MCML is a new center in which 15 PIs from the Munich area,
mainly from LMU Munich and TU Munich, will collaborate on
advancing machine learning in a data science context. Our
project is concerned with representation learning, focusing
on mathematical models of representation spaces and
representation learning for relational information.  (PhD
advisor: Hinrich Schuetze)

PHD 3: REPRESENTATION LEARNING FOR SETS OF ENTITIES

This project is motivated by the vision of a representation
of relational information that can deal with sets of
entities as an integral part of a probabilistic model.  The
goal is to represent these sets of entities as regions in
vector space, and to learn appropriate relational
transformations operating on the regions.  Relational
information will be learned from knowledge graphs and
natural language text.  (PhD advisor: Benjamin Roth)

PHD 4: MACHINE LEARNING WITH KNOWLEDGE GRAPHS

The goal of this multisite project is to extend the
capabilities of knowledge graphs in a clinical
setting. Topics include: deep learning generation models
that expose the contents off knowledge graphs in
human-readable form; and multimodal knowledge base
construction using machine learning models that infer
relations jointly from text and image input.
(PhD advisor: Hinrich Schuetze)

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