Deep program induction for personal health services

Hiring Institution

TU E-01

Technische Universiteit Eindhoven

PhD enrollment

Doctoral School of Technische Universiteit Eindhoven

YOUR TEAM: You will work in the Security and Embedded Networked Systems department of the Technical University Eindhoven.

GOAL: A personal health service, for example, in a smartphone app, helps a user to follow a care program or adopt healthier behaviors to reach certain health benefits. Typical examples are interactive self-management services for fitness or maternity, or apps and dialog systems for chronic disease management or substance abuse. A program should be engaging, lead to the target results as safely, efficiently, and conveniently as possible, and adapt to any changes on the way. Designing software that optimally meets all these goals has turned out to be very difficult.
One of the growing areas of computational intelligence is automatic programming, where a learning algorithm produces software that is executable, for example, in an abstract machine, which is a mathematical model of a computer. The abstract machine may be a universal computer or a specific sequential machine with limited functionality. The program may be learned from data (program induction) or generated based a high-level specification of the goals (program synthesis). The optimization of the program uses the knowledge that is inserted into and accumulated by the system during the operation. Program synthesis has been popular in automatic game playing and robotics. In the PhD project the goal is to develop program induction/synthesis technology for health services based on deep learning techniques. A successful candidate should have a strong background in machine learning and computer science, and good skills in programming (Python, Java).

ESR activities and expected results

The fellow will build on several interconnected competences including automata theory, machine learning, statistics, probability models, and pattern recognition.

  • Develop computable models of goal-oriented interactive health self-management systems;
  • Develop hands-on experience with ML, data analysis, and optimization techniques for personal health applications and services containing sensor data, natural language content, and user interaction data;
  • Acquire an in-depth understanding of the state-of-the-art ML techniques for Neural Abstract Machines (NAS) and program induction;
  • Create novel computational methods that combine data-driven and knowledge-driven approaches and validating them in concrete personal health propositions;
  • Explore, experiment and pilot project concepts creation for personal health
Host Timing Length Purpose
NUI Galway
1-2 weeks
Training on big data in healthcare
1-2 weeks
Training on advanced machine learning algorithms
1-2 weeks
Training on cognitive computing technology
1-2 weeks
Training on Exploitation and Dissemination of Results

Additional essential requirements

  • Master degree in Computer Science, Information Engineering (or equivalent). A degree with distinction (cum laude) is an advantage

Principal Investigator: Prof. Milan Petkovic (TU/e)
Academic PhD Supervisor: Prof. Milan Petkovic (TU/e)
Academic PhD co-Supervisor: Diego Reforgiato Recupero (UNICA)
Industrial PhD Supervisor: Aki Härmä (PHILIPS)

Main contact: Aki Härmä

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