Ph.D Dissertation Topics

Observatory Description

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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.

GIENHAS Project proposed specific solutions to improve the interaction between HEIs and SMEs. The impact of these solutions can be monitored and improved through an Observatory. Therefore, GIENAHS Observatory aims at monitoring this interaction, providing studies, data, analysis, and reports on the effectiveness of the GIENAHS models. In particular, GIENHAS Observatory has been founded on the basis of the following aspects:

  • Promotion of the collaboration and the interaction between HEIs and SMEs
  • Monitoring of the expected results of the Project and the impact of the interaction
  • Elaboration of studies and reports in which data and information relating to the interaction are collected and analyzed
  • Dissemination of the results of the monitoring activities for the benefit of all other European HEIs and SMEs that intend to improve their interaction processes

The Observatory has been developed on the basis of the standard UNI ISO 56002. This model provides guidance for the establishment, implementation, maintenance, and continual improvement of an innovation management system for use in all established organizations. In fact, project partners are applying “Open Innovation” methods. The Observatory should be used by all those Entities such as:

  • organizations seeking sustained success by developing and demonstrating their ability to effectively manage innovation activities to achieve the intended outcomes;
  • students, researchers, and other interested entities seeking confidence in the innovation capabilities of an organization;
  • associations dealing with innovation management and innovation management systems;
  • policy makers, aiming for higher effectiveness of support programs targeting the innovation capabilities and competitiveness of organizations and the development of society.

In this regard, GIENAHS Observatory will act as an “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.

In this framework, each Entity that looks for support in the design and/or implementation of innovation can launch a public call seeking a solution.

The process is the following:

  1. The SME can go to the box “Launch a challenge”, in which they can ask for a support to the international network composed by HEIs’ students;
  2. Potential Ph.D students, through the box “Submit your proposal” can apply the solution(s) they have developed for the specific Challenge.
  3. The SME will receive and evaluate the applications
  4. The Potential Ph.D student selected by the SME can contact his/her University
  5. The SME sign the Agreement (https://tinyurl.com/yz7ves6a) that regulate the creation of a customized Ph.D path on the basis on the new GIENAHS industrial Ph.D course

 

 

Observatory Services

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Within this Observatory, Project partners are going to design some services dedicated to the Organizations that have signed the Agreement:

  • Trend analysis: The Observatory could realize specific studies on interesting topics for SMEs, describing the future trends of that topic or niche. For example: Which are the innovation trends in the next 5 years in your geographical area?
  • Webinars: The Observatory could realize webinars on the importance of high-specialized human resources. For example: How an industrial Ph.D. could be useful for your company?
  • Models: The Observatory will publish schemas and templates for supporting the innovation processes in SMEs. For example, NDA contracts with HEIs, IP
  • Study visits: The Observatory will organize some opportunities to enlarge the network and improve skills from good practices at worldwide level. Some study visits will be organized in the members’ SMEs, as well as in best cases organizations.
  • Showroom area: Another section of the website will act as showroom of each company for visibility and publicity.

Each Organization will have free access to these services. They could also actively participate in the elaboration of such tools. Being part of the Observatory, in fact, means to pursue the following objectives:

  • Collecting research project proposals from SMEs and distribute within the network the information about the research opportunities;
  • Gathering the applications from young researchers, compared to the research opportunities proposed by SMEs;
  • Fostering interaction between SMEs and HEIs, as well as research project proposals and young potential Ph.D. student applications,
  • Promoting a new GIENAHS industrial Ph.D. path.

 

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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Please, also enter your Organization position
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