---------------------------------------------------------------------
Industrial PhD student Position
Artificial Intelligence and Machine Learning group
Pompeu Fabra University, Barcelona, Spain
Topic: *Interactive machine learning in online professional networks*
---------------------------------------------------------------------
We have an opening for an PhD Candidate within the research topic of
interactive machine learning in online professional networks. This
position is part of the industrial PhD program and the research project
will be developed in a company in collaboration with the university. The
position is for three years.
The present project will be conducted within the company Fluttr
(www.fluttr.in) and the Department of Information and Communication
Technologies (http://www.ai.upf.edu/). Fluttr is a professional network
that focuses on people's interests, talents and passions. Members join
one or multiple communities of interests (i.e. design, CEO community,
startups community, developers community, etc.) with the objective to
discuss, learn and discover the best professional like-minded or
complementary people, events and opportunities. The core objective of
Fluttr is to change the current recruitment market. While the job market
is rapidly changing mainly due to technology advancements, the
recruitment market has not followed suit and the CV (created in the XV
century) still represents the first fundamental milestone in the hiring
process.
*Research project description:* One of the main technological challenges
in our society is how to make sense of the unprecedented volumes of data
that are continuously generated in order to guide and support decision
making. An important limitation of the current approaches to deal with
is the lack of a learning process that can (1) obtain and evaluate
feedback of the system’s performance from the environment and (2) adapt
accordingly to improve the future performance. This is the basis
Reinforcement learning (RL), the area of machine learning concerned with
learning to control a system (or agent) that interacts with an
environment so as to maximize a performance measure that expresses a
long-term objective. What distinguishes reinforcement learning from
other machine learning paradigms is that only partial feedback is given
to the learner about the learner's predictions. Further, the predictions
may have long term effects through influencing the future state of the
system.
Fluttr generates and uses large amounts of complex data from different
sources that is used to build matches to create professional
opportunities. Examples of matches are between members of a same
community or different ones, between members and available jobs, provide
pro-active recommendations for local events, training courses and
products (for example books) that might be of interest to each member.
The present project proposal addresses the challenges faced by the
recommendation engine of Fluttr. The first objective is to collect and
study behavioral patterns and user profiles to extract knowledge and
create a first predictive model. The model is going to form the basis of
the learning systems developed in the following objectives:
- Integrating recommended matches (actions) in the predictive model and
analyzing how these action spaces with different structures (e.g.,
actions organised in a graph) and different levels of interaction
between actions (e.g., similarity between actions) may influence the
learning performance.
- Developing efficient algorithms that work in non-stationary
environments when not only the performance of each action evolves, but
also the structure of the action space can change over time.
*Your Profile: *We are looking for highly motivated, talented and
enthusiastic candidates with an active attitude and excellent
communication and collaborative skills. The successful applicant has
completed a master degree in data science or related field. The
candidate is expected to understand contemporary topics and issues in
Data Science. For example, multivariate statistics, machine learning,
data mining and algorithmic complexity. An interview will be part of the
selection procedure.
*Information and application:* Questions regarding this positions can be
directed to Vicenç Gómez <[email protected]> or Gergely Neu
<[email protected]>. To apply for this position, please subscribe to
the open call in this website before February 15th, 2017:
http://doctoratsindustrials.gencat.cat/en/projects/index
_______________________________________________
uai mailing list
[email protected]
https://secure.engr.oregonstate.edu/mailman/listinfo/uai
