Ph.D Dissertation Topics

Observatory Aims

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Research, basic and applied, deepens the examination of problems and generates new insights. In the other hand, technology is the outcome of the application of scientific expertise to achieve a practical advantage of a product or service. The worlds of science and technology are widely agreed to be connected to the power of creativity and the determination of entrepreneurship. The utilization of a research result for the production of an innovative product can be achieved either through a business initiative of the researcher or through his cooperation with the appropriate company/industry. The first way requires business skills and devotion to the commercial exploitation of the concept of the researcher at the cost of the study he conducts. Instead, the latter is focused on the researcher and the business/industry’s harmonious cooperation, where everybody provides more of mutual benefit in the area they know best.  This requires an even better MATCH between SMEs and HEIs: SMEs need to develop their internal skills to tackle innovation in a more structured and systematic way, while HEIs need to improve their ability to interact with SMEs so that researchers also see a real career perspective within the media.

The aim of this Observatory is to develop a sort of “Open Innovation Marketplace” connecting SMEs seeking solutions to important challenges they face (research, innovation, needs) with an international network of young persons/students – potentially Ph.D students both from Universities and Industry – that can offer different perspectives and fresh insight. This creates a bridge between the needs of SMEs and the offer of innovation. This matching will be translated into a new industrial Ph.D path: the GIENAHS Ph.D courses.

Within this Observatory, Project partners are going to design the following services:

  • Trend analysis: studies on interesting topics for SMEs. For example: Which are the innovation trends in your geographical Area?
  • Webinars: on the importance of high-specialized human resources. For example: How an industrial PhD could be useful for your company?
  • Models: schemas and templates for innovation development. For example: NDA, contracts with HEIs, IP
  • Study visits: opportunities to enlarge the network and improve skills from good practices at worldwide level
  • Showroom area: another section of the website, acting as showroom of each company for visibility and publicity

 

Ph.D Topics

Cost of energy optimization for the operation of microgrids based on demand response techniques

Description

This dissertation focuses on the development and evaluation of advanced demand response techniques in almost zero energy buildings and microgrids. A detailed investigation and analysis of the energy efficiency of a standard residential building and a standard industrial building (/ office) need to be done in principle. For the evaluation of the energy efficiency of these buildings, it is necessary to develop and use a methodology that includes the taking and utilization of measurements of indoor and outdoor environmental conditions, energy consumption, and electricity generation from RES. In addition, it is necessary to develop a model for dynamic simulation of building installations using software. The aim is also to develop a method of short-term forecast (with a time horizon of 24 hours) of electricity consumption and electricity generation from RES using models of Artificial Neural Networks. The method is suitable not only for the extraction and evaluation of results both at the building level and at the microgrid level, but also for the export of balanced solutions for reducing electricity costs and shifting loads at the group and microgrid level.

Requirements

Academic entry qualification overview:

  • A First or Upper Second Class Bachelor’s degree (or its international equivalent).
  • A relevant master’s degree.

Candidates whose first language is not English require one of the following certificates:

  • IELTS test minimum scores – 7 overall, 7 writing, 6 other sections
  • TOEFL (internet-based) test minimum scores – 100 overall, 25 writing, 22 other sections
  • Pearson Test of English (PTE) minimum scores – 66 overall, 66 writing, 59 other sections;

Duration

3 years.

A deep learning approach to 2D/3D object affordance understanding

Description

The ability to recognize the objects around us and to utilize the rich visual information that characterizes them, is a major challenge for the field of computer vision. Objects are key elements for a wide range of applications ranging from understanding scene features and automation to security and robotics. In recent years, significant steps have been taken to locate and identify 2D / 3D objects using deep learning techniques, accompanied by significant improvements in computing power. However, finding effective algorithms for understanding the characteristics of an object remains an open challenge, as existing research focuses mainly on the appearance characteristics of objects, such as shape and color, ignoring their functionality. In this dissertation, the aim is to develop models and techniques for understanding the functionality of objects, which defines the ways in which these objects can be used by humans.

Requirements

Academic entry qualification overview:

  • A First or Upper Second Class Bachelor’s degree (or its international equivalent).
  • A relevant master’s degree.

Candidates whose first language is not English require one of the following certificates:

  • IELTS test minimum scores – 7 overall, 7 writing, 6 other sections
  • TOEFL (internet-based) test minimum scores – 100 overall, 25 writing, 22 other sections
  • Pearson Test of English (PTE) minimum scores – 66 overall, 66 writing, 59 other sections;

Duration

3 years.

Οptimization of scheduling in workflow management systems using reinforcement learning

Description

A workflow is defined as the execution of a sequence of tasks, the order of which is determined by data interdependencies and the target outcome. The execution of workflows can be managed by a Workflow Management System (WMS) which undertakes the scheduling of tasks, handling of failures and monitoring health status. Scheduling workflows involves mapping tasks to available execution sites in respect to a cost function that optimizes an objective such as the total execution time. In the era of multi-core architectures, accelerated computing is largely driven by scaling across distributed resources. For a WMS, identifying and exploiting opportunities for parallelism becomes a critical necessity. At the same time, distributed execution introduces new challenges as workflow execution should be agnostic to the software and hardware on the execution sites. Additionally, machine learning based approaches to scheduling optimization have not been explored to the same extent as heuristic algorithms. The aim of this dissertation is to exploit opportunities for parallelism and leverage machine learning in scheduling scientific workflows across distributed heterogeneous execution sites.

Requirements

Academic entry qualification overview:

  • A First or Upper Second Class Bachelor’s degree (or its international equivalent).
  • A relevant master’s degree.

Candidates whose first language is not English require one of the following certificates:

  • IELTS test minimum scores – 7 overall, 7 writing, 6 other sections
  • TOEFL (internet-based) test minimum scores – 100 overall, 25 writing, 22 other sections
  • Pearson Test of English (PTE) minimum scores – 66 overall, 66 writing, 59 other sections;

Duration

3 years.

Launch a challenge

Submit your Proposal

Through this form, you can launch a challenge where you can ask for support. The Challenge will be a sort of “research topic”  addressed to potential new Ph.D students. Please explain what your problems are, trying to summarize what are you looking for, from innovative point of view.

Through this form, you can submit a potential “solution”, an innovative idea that solves a problem. This proposal can be a reply to a published challenge or can be submitted even without a published challenge.

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