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PhD supervisors from academic institutions in EMEA are invited to submit their proposals for collaborative research projects with Microsoft Research Cambridge.
Applications are then peer reviewed and a number of projects selected for funding. PhD students are appointed to the selected projects and begin their research in the following academic year under the supervision of their academic supervisor, with co-supervision from a researcher at Microsoft Research Phd dissertation factors affecting construction costs. The Microsoft Research PhD Scholarship Programme in EMEA Europe, Middle East, Africa was launched in and has so far supported more than PhD students at institutions, phd dissertation factors affecting construction costs.
Microsoft actively seeks to foster greater levels of diversity in our workforce and in our pipeline of future researchers. We are always looking for the best and brightest talent and celebrate individuality. We invite candidates to come as they are and do what they love.
Applications must not be made by students but by PhD supervisors, who must have been nominated by Microsoft Research prior to the submission deadline. Only applications from institutions in Europe, the Middle East, and Africa will be considered. We are always looking for the best and brightest talent and pride ourselves on our individuality, phd dissertation factors affecting construction costs. University PhD Supervisors wishing to submit an application to the Programme will need to have been nominated by Microsoft Researcher.
Applications must be submitted by academic institutions, such as from a PhD supervisor or departmental secretary as we do not accept applications from students directly.
Selection Criteria Applications should contain the following: A completed online application form that includes contact details of the main supervisor together with a named secondary supervisor who will be willing to take the project on if the main named supervisor leaves.
Funding requirements. A project proposal of maximum six 6 A4 pages in point font, including references. Accepted formats: plain text. txt.
pdf, or Microsoft Office Word. doc or. docx only. Applications are sent for comprehensive review to Microsoft internal reviewers only. Ranking depends on review feedback. Other factors, such as relevance to Microsoft research themes, are taken into account for the selection.
Each Microsoft scholarship consists of an annual bursary up to a maximum of three years or a maximum of four years for an EPSRC CASE award. The monetary value of the award varies by country to reflect local differences in costs and overheads. Payment is made directly to the institution. The amount of the scholarship is the maximum phd dissertation factors affecting construction costs Microsoft Research pays to the institution.
In addition, every Scholar receives a fixed hardware allowance and conference allowance. All PhD Agreements, Student Description Forms and Visitor Agreements will be issued via our online contracting processing service — Docusign In our efforts to reduce paper consumption and the amount of time shipping agreements can often take, we will be utilizing an phd dissertation factors affecting construction costs contract processing service, Docusign for execution of all PhD Agreements.
This tool allows for the collection of legally verifiable eSignatures from designated authorized signers simultaneously. Once the agreement is signed in full, all parties on the record receive the fully executed PDF document via email for their files. If your organization is not comfortable electronically signing agreements the tool still allows for you to download the agreement, print, sign, scan and upload back into the tool. Once uploaded the agreement will be routed to the next signer on the list and again once signed in full, a fully executed PDF will be sent to all parties, phd dissertation factors affecting construction costs.
Further phd dissertation factors affecting construction costs will be sent once the selection has been made. Note: All intellectual property rights associated with funded PhD projects are subject to the terms and conditions in the PhD Term Sheet and EPSRC Term Sheet where appropriate.
The information provided in the application is used for making our decision for the Microsoft Research PhD Scholarship only. We may pass this information to external academic reviewers who have agreed to help us review the applications. All information contained in the application shall be considered by Microsoft to be non-confidential.
Universities should not submit information that is confidential, restricted, or sensitive in any way and Microsoft assumes phd dissertation factors affecting construction costs responsibility for protecting or disclosing any such information, once submitted. Microsoft is committed to protecting your privacy. Read the Microsoft Online Privacy Statement. The following applications have been selected for funding starting in the academic year — Supervisor: Amos Storkey, University of Edinburgh, UK.
Summary: Deep reinforcement learning RL has had huge empirical success and is a major enabling technology for many applications of AI. However, recent RL algorithms still require millions of samples to obtain good performance.
Since obtaining environment interactions is often costly and since challenging environments are rarely static, this inhibits many practical applications.
This project will investigate ways of reducing this cost, aiming to find more sample-efficient RL algorithms. We aim for the algorithms to be deployable in realistic settings, phd dissertation factors affecting construction costs, where agents use deep networks to represent knowledge about the environment.
It is also likely to lead to improved performance of other systems making automated decisions. Supervisor: Gavin Doherty, Trinity College, Dublin. Summary: Health self-report or self-monitoring activities, such as mood logging, are a central part of many treatments for mental disorders.
Mood logs need to be recorded regularly to be beneficial, yet people can find it difficult to stay engaged with them over time. Phd dissertation factors affecting construction costs logging is an example of a health status reporting task, and while mental health is a huge issue, management and self-management of many other health conditions also involve some form of health status reporting.
We propose that health status reporting systems based on speech and conversational user interfaces CUI have potential advantages in terms of accessibility, engagement, and disclosure.
Speech as a modality can be natural and convenient for users, phd dissertation factors affecting construction costs, and may support users to disclose their feelings and help create a reporting experience that is engaging and lightweight. The act phd dissertation factors affecting construction costs speaking aloud might phd dissertation factors affecting construction costs increase the sense of unburdening associated with mood disclosure, and help provide an experience which is cathartic.
The aim of this PhD is to explore the feasibility of conversational user interfaces CUIs as a means for supporting health self-reports of mood logging, while gathering design based insight for the development of such systems.
The PhD research will establish the feasibility of this approach to mood logging, together with a detailed exploration of the design space, identification of appropriate strategies, and a carefully designed best-of-breed example of a conversational health status reporting interface. The provision of richer, natural language based health status reporting data also opens phd dissertation factors affecting construction costs opportunities for further research on the application of machine learning to mental health status data.
Supervisor: Joe Finney, Lancaster University, UK. Summary: This project proposes the creation of new tools and processes phd dissertation factors affecting construction costs will enable embedded hardware devices to be successfully manufactured in low volumes — thousands of units and fewer.
This is important in a world where citizen developers are becoming empowered to design new hardware solutions ranging from innovative interactive devices to IoT sensing and control systems.
By engaging with Microsoft Research and other industrial partners, this PhD project will start by identifying requirements common to the reproducible manufacture of a range of embedded hardware devices. From these hardware tooling design patterns that support low volume yet reproducible manufacturing will be proposed, built and evaluated.
The ultimate aim is to create a set of freely available open tools and processes that others can use and build upon, to unlock a long tail of hardware devices.
Supervisor: Thomas Bohné, University of Cambridge, UK. Our research is motivated by recent technical developments enabling digital representations of humans that are visually and behaviourally high-fidelity up to indistinguishable from real humans. The potential impact of these virtual humans on society is expected to be substantial and cannot be understood without new research.
Our research team is specifically interested in Avatar-Agent-Hybrids, which can be controlled either by a human or by an AI. Avatar-Agent-Hybrids offer new research opportunities in which humans cannot distinguish if the digital representations they interact with are human or artificial agents.
As this type of interaction is likely to affect especially how humans work, our project focuses on work-related situations. By developing a novel experimental setup in virtual reality VRwe will simulate — in real-time — realistic social and work contexts in which we can study the effects of visually and behaviourally indistinguishable human avatars on humans and their social interaction. This will allow our research team to gain ground-breaking and fundamentally new cognitive and behavioural insights into the effects of AI on humans.
Supervisor: Diego Perez Liebana, Queen Mary University of London, UK. Summary: The latest breakthroughs in game playing AI have primarily focused on the application of Reinforcement Learning RL and Statistical Forward Planning SFP methods, phd dissertation factors affecting construction costs, such as Monte Carlo Tree Search MCTS and Rolling Horizon Evolutionary Algorithms RHEAphd dissertation factors affecting construction costs, to games, phd dissertation factors affecting construction costs.
Advances in Go, Chess, Shogi, Atari and Starcraft have been achieved by combining Deep Learning and different forms of model-free or model-based RL, phd dissertation factors affecting construction costs, including variants of MCTS. Model-based and SFP algorithms require access to an internal model of the game that allows agents to reason about the future, enabling a more data-efficient and flexible behaviour than those learnt with model-free Deep RL.
However, in many real world applications and complex games such as many created in the games industrythis model is non-existent, not available, or computationally too expensive to use. There is a prominent line of research that currently aims at automatically learning these models from interactions with the environment, showing that learning a forward model provides an important boost for action decision making. However, the scenarios normally employed in the literature are simple.
Using the Malmo platform, this proposal focuses on approximating forward models in complex games by learning local interaction functions, to then investigate the use of SFP algorithms in these domains.
Specifically, this research aims at 1 learning local forward models in 3D environments with partial observability; 2 implement SFP methods that would plan over said learnt models; and 3 investigate how the presence of non-stationary policies of other agents affect both model learning and SFP performance.
Supervisor: Matthew Foreman, Imperial College, UK. Summary: Measuring birefringence in 3D is important for many applications including optical data storage and cell biology.
The current state-of-the-art techniques use methods that are designed to measure thin 2D structures but have been adapted in ad-hoc ways and combined with advanced processing to recover 3D information. Remarkably, there is an important and basic research question that still exists: is there a physically correct way to isolate and measure the birefringence of a thin 2D layer that is contained within a 3D volume, and therefore correctly reconstruct a full 3D volume?
This PhD research project will answer this question. Supervisor: Harish Bhaskaran, University of Oxford, UK. Summary: The use of functional materials that can accumulate information to carry out both memory and computing tasks in-situ is a growing field. Using optical integration onto silicon chips provides a unique opportunity to combine the benefits of silicon scaling and the wavelength multiplexing of optics onto a single platform.
In this proposal, we will significantly expand our initial work on carrying out non-von Neumann computations such as Vector-Matrix Multiplication directly in hardware to a larger matrix to prove a lab scale demonstration of the potential for such hardware in real-world computing tasks. In addition, this proposal will aim to benchmark such computations against existing state-of-the-art in terms of energy and operational speed.
Supervisor: Ben Glocker, Imperial College London, UK. Summary: Being able to detect and communicate when a predictive model fails is of utmost important in system that are used for clinical decision making. In this PhD research project, novel mechanisms for failure detection and inferring causes for failure will be developed and tested on large scale population data. It is expected that the research leads to high impact innovation that is critical for deploying learned based systems and services in healthcare settings.
Supervisor: James Locke, University of Cambridge, UK. Summary: Programming biological systems in cell populations is a key goal of synthetic biology. To achieve this, it is critical to understand how synthetic genetic circuits behave in individual cells.
Can You Fail Your Dissertation? Can You Fail Your Doctorate / PhD?
, time: 11:05VIDEO PREMIERE: Cavern Deep, "Deeper Grounds" Live-in-Studio
Jun 10, · Cavern Deep on “Deeper Grounds”: “ Deeper Grounds is the 6th track of our upcoming concept album, the song is about the point where the expedition into the cavernous realm realize that there is no way to go except further down into the abyss. The lyrics are as follows: Deeper Deeper Deeper Deeper. This live recording was made at our bassist Max’s studio-rehersal in Umeå They will discuss the future plans for the course of study, the structure of the supervision and any factors affecting the progression of the PhD project. Next, the student will give a 20–minute presentation based on the following recommendations There is a real drive in the industry to make memory safety vulnerabilities a thing of the past. Project Verona is a highly ambitious research project to make that a reality for the infrastructure we build for the cloud. This Ph.D. scholarship will apply world-class research on semantics and type systems to the design of Project Verona
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