University of Southampton, England invites online Application for number of Fully Funded PhD Positions at various Departments. We are providing a list of Fully Funded PhD Programs available at University of Southampton, England.
Eligible candidate may Apply as soon as possible.
(01) PhD Positions – Fully Funded
PhD position summary/title: Wavelength-flexible, single-frequency fibre lasers
Fibre lasers have seen a rapid development in output power and performance over the past three decades and have revolutionised the application space for photonics. This project is focused on the development of wavelength-flexible single-frequency fibre sources to support and facilitate a plethora of emerging exciting practical applications in science.
Deadline : 31 Mar 2026
(02) PhD Positions- Fully Funded
PhD position summary/title: Computational modelling of reacting multiphase flows in methane pyrolysis for sustainable hydrogen production
This project on methane pyrolysis, a disruptive route to low-cost sustainable hydrogen and valuable carbon black, aims to tackle extreme high-temperature reactive flows and multiphysics challenges with cutting-edge computational fluid dynamics (CFD). It will apply fluid dynamics, heat and mass transfer and high-performance computing to design intensified reactors that could transform the UK and global hydrogen economy.
Deadline : 31 Jan 2026
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(03) PhD Positions – Fully Funded
PhD position summary/title: Development of high-performance miniaturised ultrasonic devices for precision minimally invasive surgery
This project, in the field of ultrasonic surgery, focuses on the development of cutting-edge miniature ultrasonic devices targeted for bone surgery. It seeks to advance the current state-of-art design by introducing new configurations and incorporating novel structures in miniaturised devices to enhance precision and improve clinical outcome.
Deadline : 30 Jun 2026
(04) PhD Positions – Fully Funded
PhD position summary/title: Development, optimisation, and validation of µCT-based 3D X-ray histology for advanced clinical and biomedical applications
This PhD project will develop, optimise, and validate three-dimensional (3D) X-ray histology using micro-computed tomography (µCT) to transform how tissue samples are analysed in both clinical diagnostics and biomedical research.
Conventional histopathology relies on slicing tissues before microscopic analysis, which risks missing critical structures. In contrast, µCT allows for non-destructive, whole-tissue imaging—offering detailed 3D insight before physical sectioning. This project aims to create imaging workflows that guide sampling and sectioning with greater precision, reducing diagnostic errors and supporting advanced downstream analyses such as immunohistochemistry and spatial multi -omics.
You will design and validate new protocols for both fresh and formalin-fixed paraffin-embedded (FFPE) tissue imaging, assess diagnostic value, such as tumour margin assessment, and evaluate the impact of X-ray exposure on tissue quality for molecular analysis. A dedicated work package will investigate the potential of this technology to support tissue microarray construction, sample sharing, and cost recovery in biobanking.
Deadline : 30 Nov 2025
(05) PhD Positions – Fully Funded
PhD position summary/title: System identification of nonlinear space structures via physics-informed machine learning
Modern space systems, from spacecraft components to precision sensors, operate in extreme and hostile environments. To meet stringent performance demands while minimising payload mass, ultra-lightweight high-performance structures are increasingly employed in space missions. Although such advanced structures offer exceptional capabilities, they often exhibit nonlinear dynamic behaviours which cannot be captured by employing classical linear models. Such behaviours arise from geometric nonlinearities, friction, contact, and complex damping mechanisms, all of which critically impact the performance, stability, and reliability of space structures. In this context, developing novel tools for the analysis, identification, and prediction of the dynamics of nonlinear systems is essential for designing, testing, and validating the next generation of space technologies.
This project will combine numerical modelling, advanced analytical techniques, and experimental methods to develop a novel approach for the identification of nonlinear systems. Specifically, this project aims to:
- develop a novel nonlinear system identification method based on physics-informed machine learning approaches, capable of producing computationally efficient reduced-order models and enabling accurate, efficient modelling of complex space structures
- investigate the accuracy and extrapolation capabilities of the identified reduced-order models, identifying pros and cons of the proposed approach
- validate theoretical and numerical results through state-of-the-art experimental facilities
Deadline : 31 Aug 2026
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(06) PhD Positions- Fully Funded
PhD position summary/title: Development of a low-cost wearable system for respiration monitoring using near-infrared spectroscopy (NIRS)
Respiratory diseases are a major health concern in the UK, affecting one in five individuals and costing approximately £188 billion annually. Current monitoring methods, such as manual counting, respiration belts, and end-tidal carbon dioxide (EtCO2) measurement, have limitations in accuracy and comfort and are unsuitable for long-term continuous monitoring.
Continuous respiratory monitoring is crucial as many conditions develop progressively. Wearable medical devices offer a promising solution for real-time and continuous health tracking, but an ideal, cost-effective, and comfortable long-term respiratory monitoring system is lacking. Near-infrared spectroscopy (NIRS) uses light to interrogate the optical properties of tissue. The dynamical changes of these properties are closely related to physiological signals such as respiration, heart rate, and tissue oxygenation.
This project aims to investigate the feasibility of long-term respiration monitoring using NIRS and to develop a wearable monitor using NIRS technology. The objectives are:
- simulating light transport in the thorax to understand the source of signals and optimise monitoring
- designing and building a low-cost wearable sensor for lung health monitoring
- testing the feasibility of continuous monitoring with the wearable sensor in a healthy adult cohort
- identifying and classifying waveform patterns and breathing parameters using artificial intelligence (AI)
Deadline : 12 Dec 2025
(07) PhD Positions – Fully Funded
PhD position summary/title: AI-based virtual microphone technique for automotive applications
The field of automotive noise control is growing rapidly, with active noise reduction inside car cabins emerging as a major research focus. Automotive active noise control systems aim to create carefully designed sound fields (anti-sound) using car-mounted loudspeakers to cancel unwanted noise. While effective at controlling engine noise due to its tonal nature, these systems face challenges with broader noise sources like road noise, as controlling sound fields over large spaces is difficult at high frequencies.
This limitation can be overcome by enabling localized active noise control around the driver’s and passengers’ ears. This can be achieved without the use of in-ear microphones. A crucial step is therefore estimating the sound pressure at the ears. The goal of this project is to develop AI-based technology that predicts sound pressure at the listener’s ears using signals from microphones positioned near, but not at, the ears; for example, on seat headrests.
Deadline : 28 Feb 2026
(08) PhD Positions – Fully Funded
PhD position summary/title: Developing next-generation marine sensors using intracavity absorption spectroscopy for advanced ocean monitoring
This cutting-edge technique, proven for hydrocarbon detection, will be optimized to detect specific analytes or pollutants. Key aspects include:
- designing sensor materials and ensuring compatibility with permeable membranes
- adapting sensors for varying environmental conditions like temperature, water type, and depth
- integrating sensors with sea vehicles considering payload size, depth, and battery constraints
- addressing cross-sensitivity to enhance detection accuracy
Deadline : 31 Dec 2025
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(09) PhD Positions – Fully Funded
PhD position summary/title: Development of a miniaturised plug-and-play in-situ plasma measurement instrument for small satellites
Small satellites, especially CubeSats, are transforming space missions, from Earth observation to deep space exploration. However, over half of CubeSat missions fail, often due to system malfunctions caused by the harsh and unpredictable space environment. Understanding and mitigating these failures is critical to improving mission success.
This project addresses the challenge by developing PlasmaCube, a real-time in-situ plasma measurement payload based on the Langmuir probe principle. The system will include optimally designed electrodes, nano-level current measurement electronics, a control system, and a robust data collection unit. The goal is to create a standardized, plug-and-play diagnostic payload for CubeSats and other small satellite platforms.
Deadline : 31 Jul 2026
(10) PhD Positions – Fully Funded
PhD position summary/title: Novel Phase Change Materials for integrated photonics
The current increase in data generation is expected to reach unsustainable rates by the end of the decade. This has a strong impact on the environment and therefore new solutions are sought after. The project work is to build the most efficient components by developing the next generation of advanced materials to achieve sustainability in AI applications.
Deadline : 31 Aug 2026
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(11) PhD Positions – Fully Funded
PhD position summary/title: Large area 2D semiconductor platforms
2D semiconductors offer the solution as they can be scaled to the molecular level and create in-memory computing components one of the key elements for neuromorphic computing the hardware that will support the next generation of artificial intelligence.
The project aims to create a revolutionary semiconductor platform using 2D materials to enable electronic, photonic and energy application while unlocking the ultimate limit in miniaturisation of semiconductors. You will benefit from state-of-the-art custom large area 2D equipment not available anywhere else and from one of the most advanced university cleanrooms in the UK.
Deadline : 31 Aug 2026
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(12) PhD Positions – Fully Funded
PhD position summary/title: A Quantum Leap for Quantum Technology
This PhD project focuses on developing custom Hollow Core Fibres (HCFs) tailored for quantum computers, repeaters, memories, and secure quantum communication systems. HCFs offer ultra-low loss at non-telecom wavelengths – ideal for single-photon sources and detectors – making them a game-changer for real-world quantum applications.
Deadline : 31 Jul 2026
(13) PhD Positions – Fully Funded
PhD position summary/title: Shaping the Future of Global Communications with Hollow Core Fibres
Are you a graduate in physics, engineering, materials science, chemistry, or a related discipline? Do you want to be part of a technological revolution that could redefine how the world communicates? Join our cutting-edge PhD project at the Optoelectronics Research Centre (ORC) – in collaboration with Microsoft Azure Fiber
Join our cutting-edge PhD project to help develop the next generation of Hollow Core Fibres (HCFs). These innovative fibres guide light through air rather than glass, offering the potential to dramatically increase the speed, efficiency, and performance of global data transmission.
Deadline : 31 Jul 2026
(14) PhD Positions – Fully Funded
PhD position summary/title: Grating waveguide structures for high-power lasers
Novel dispersive photonic devices are opening up incredible possibilities for efficient lasers and light manipulation. We’re looking for a passionate PhD student to join a Marie Skłodowska-Curie Action doctoral training network and push the boundaries of what’s possible with nanofabrication technology.
Nanofabrication covers groundbreaking techniques to structure materials at dimensions smaller than the wavelength of visible light, enabling new light-matter interactions via a metasurface structure coupled to a waveguide. By advancing our understanding and development of scalable nanofabrication processes on a silicon platform, you’ll be at the forefront of creating the next generation of advanced optical components enabling novel laser systems for future photonics applications.
Deadline : 31 Mar 2026
(15) PhD Positions – Fully Funded
PhD position summary/title: Dielectric all-crystalline grating waveguide reflectors
Novel crystalline photonic devices are opening up incredible possibilities for efficient lasers and light manipulation. We’re looking for a passionate PhD student to join a Marie Skłodowska-Curie Action doctoral training network and push the boundaries of what’s possible with pulsed laser deposition.
Pulsed laser deposition (PLD) is a ground-breaking technique that uses light to create new materials and waveguide devices. By advancing our understanding and development of scalable crystal structures, coupled with patterning them at the nanoscale, you’ll be at the forefront of creating the next generation of advanced optical components enabling novel laser systems for future photonics applications.
Deadline : 31 Mar 2026
(16) PhD Positions – Fully Funded
PhD position summary/title: Starfire: simulating thermonuclear ignition on neutron stars
This project investigates thermonuclear (Type I) X-ray bursts on neutron stars through numerical simulations of flame spreading and ignition. You’ll model burst dynamics, compare results with observations, and explore broader applications of the code to stellar flame propagation and exoplanetary atmospheres, developing strong computational and programming expertise.
Deadline : 6 Jan 2026
(17) PhD Positions – Fully Funded
PhD position summary/title: The evolution of compact binary stars: next generation population synthesis
Most stars are members of binary systems. Many of the most interesting astrophysical systems, from Type Ia supernovae to the black-hole mergers observed by LIGO, only exist as products of binary evolution. In almost all of these systems, one or both binary components are compact objects (white dwarfs, neutron stars or black holes). Despite their importance, the evolution of compact interacting binary stars remains poorly understood. For example, we still don’t even know the dominant pathway(s) for producing Type Ia supernovae, even though we routinely use these objects as cosmological standard candles.
The problem is that several critical physical processes for binary evolution are extremely difficult to model accurately and self-consistently. Much of what we have learned about these systems has come from “population synthesis” studies, in which the properties of the detectable populations of these systems are predicted via numerical simulations.
In this project, you will develop a next-generation population synthesis data base and framework for compact binary systems. A unique feature of your work will be an emphasis on adopting and testing state-of-the-art physical and theoretical constraints on all key physical processes. For example, you will use the latest research on the spin down rates of single and detached binary stars to inform your modelling of interacting compact binaries. You will then exploit this framework to predict the populations of white dwarf, neutron star and black hole binaries, from those that can be observed both electromagnetically to those that will be seen with gravitational wave detectors.
Deadline : 6 Jan 2026
(18) PhD Positions – Fully Funded
PhD position summary/title: The forgotten population of gravitational wave sources
The detection of gravitational waves (GWs) has been a huge breakthrough in physics. Today’s GW detectors are located on Earth, but the next big milestone will be a space-based GW observatory called “LISA”. In this project, we will study a crucial, but overlooked, GW source population for LISA: contact binaries.
The detection of gravitational waves (GWs) from merging black holes and neutron stars has been one of the greatest breakthroughs in (astro)physics in recent years. The next milestone in this field will be the launch of the space-based LISA mission. LISA’s sensitivity to low-frequency GWs will allow it to detect (for the first time) close binary stars in the Milky Way.
In preparation for LISA, huge effort is currently being dedicated to estimating the number and properties of these binary systems. However, essentially all of this effort has so far been dedicated to compact binaries, such as systems in which one of both components are black holes, neutron stars or white dwarfs. What has been overlooked is the one important population of close binaries in which neither component is compact: the contact binaries. These are composed of “normal” stars that have been forced so close together that they share a single dumbbell-shaped envelope.
In this project, you’ll construct the most up-to-date compilation of contact binaries, determine the number of these systems in the Milky Way and carry out simulations to predict the gravitational wave signals they will produce in LISA.
Deadline : 6 Jan 2026
(19) PhD Positions- Fully Funded
PhD position summary/title: Accretion disk winds in quasars
All quasars are powered by the same central engine: a supermassive black hole that is fed by a luminous accretion disk. Approximately 15% of all quasars exhibit clear evidence for powerful outflows driven from these disks, in the form of broad, blue-shifted absorption lines. However, these systems are the tip of the iceberg. They represent just the sub-set of quasars viewed at a particularly favourable orientation. In reality, all quasars are likely to drive such winds.
This is important, because these outflows provide a key feedback mechanism: they can remove significant amounts of mass, energy and angular momentum from the quasar and inject it into the surrounding (inter-)galactic medium. Despite this, we know almost nothing about these accretion disk winds. For example, the geometry, kinematics, and even the basic driving mechanism responsible for launching them are still basically unknown.
This project aims to remedy this situation by modelling the wind-formed observational signatures of quasars. This work will be carried out in the context of an established collaboration and will use an existing, state-of-the-art Monte Carlo radiative transfer code. The ultimate goal is to determine the fundamental parameters of quasar accretion disk winds and thus shed light on how they regulate the fuelling of supermassive black holes and the feedback between quasars and their environments. In addition, You will test a wind-based quasar unification scenario: is it possible that most observational signatures we associate with quasars are actually shaped by disk winds?
Deadline : 6 Jan 2026
(20) PhD Positions – Fully Funded
PhD position summary/title: Damage assessment of aircraft by machine learning approaches
This research focuses on the visual inspection of aircraft images in digital format. It involves identifying various types of defects and training these images using machine learning techniques. The goal is to develop a tool that assists maintenance engineers during checks by indicating what actions are needed next, such as assessing the criticality of a defect. In addition, the research will deliver a cost benefit analysis of the whole process, to evaluate the actual benefits from such digitalized and automated tool.
Deadline : 12 Dec 2025
(21) PhD Positions – Fully Funded
PhD position summary/title: AI-enhanced operation and maintenance of offshore wind farms: towards resilient energy systems
Offshore wind farms are central to global decarbonisation and renewable energy strategies. However, their operation and maintenance (O&M) costs account for up to 30% of total lifecycle expenditure, with unplanned downtime threatening reliability and profitability. Traditional O&M strategies are often reactive, lacking predictive insight and adaptability to complex offshore environments.
This project will investigate how artificial intelligence (AI) can transform offshore wind O&M by advancing automated risk analysis, predictive maintenance, and decision-support frameworks for resilient energy systems. The research will focus on integrating data-driven models, sensor data (SCADA, condition monitoring), and advanced algorithms to forecast failures, optimise inspection schedules, and improve system resilience. Particular attention will be paid to uncertainty quantification, human–AI collaboration, and the scalability of solutions for large wind farm deployments.
The intended outcomes include developing innovative AI-enabled tools that reduce costs, minimise downtime, and strengthen resilience of offshore renewable energy systems. The project also aims to contribute new knowledge at the intersection of AI, risk analysis, and offshore energy engineering, with applications extending to digital twins and smart maintenance strategies.
Deadline : 15 Dec 2025
(22) PhD Positions – Fully Funded
PhD position summary/title: Sustainable Generative AI models
As GenAI becomes increasingly central to a wide range of applications, from generating images to generating videos and music, their computational demand and the time required for training and inference have escalated. This research seeks to address these challenges by developing innovative techniques for efficiency, including architectural innovations, compression strategies, algorithmic improvements, and system level optimizations.
The goal is to enable the deployment of state-of-the-art GenAI models across broader scenarios of computing environments, from high-end servers to consumer-level machines.
This project will contribute to making GenAI more democratic, efficient, and scalable, paving the way for their application in real-time and resource-constrained scenarios. The main research objectives are:
- to develop cutting-edge techniques for model compression, such as pruning, quantization, and knowledge distillation, tailored for GenAI models
- to design and experiment with new GenAI architectures that are more efficient, requiring less computational power and memory
- to create new algorithms and system wide optimizations to accelerate both training and inference processes for GenAI, making them more suitable for deployment across a variety of computing environments
- to develop and utilize benchmarks and metrics specifically designed to evaluate the energy-aware efficiency and performance of GenAI under various computational constraints.
Deadline : 27 Jan 2026
(23) PhD Positions – Fully Funded
PhD position summary/title: Dynamical black hole mass measurements at high redshift with GRAVITY+
Recent observations suggest that quasars in the early universe have SMBHs that are too massive to form in the short time since the big bang, shaking the foundations of cosmology and our understanding of black hole growth. The main problem is that we cannot be sure that the methods employed to estimate those SMBH masses are reliable in the conditions of the early universe. GRAVITY+ will deliver transformational capabilities to measure spatially resolved, dynamical supermassive black hole masses up to redshift 4.5 and possibly beyond. Our first data from such a high redshift quasar indicate that current mass estimates may indeed be out by a factor of 10.
Using these new GRAVITY+ measurements, we will be able to recalibrate early universe measurements and deliver precise and accurate SMBH masses. As part of this project, you’ll work on GRAVITY+ data of quasars between redshift 1 < z < 4.5, perform dynamical modelling to determine the SMBH masses, and compare those measurements to estimates from more generic methods used in the early universe. With this comparison, you’ll be able to characterise how high-luminosity, fast growing objects are offset from local sources and how early universe mass estimates need to be corrected.
Deadline : 31 Jan 2026
(24) PhD Positions – Fully Funded
PhD position summary/title: Nanostructured neural architectures for sustainable neuromorphic computing
You will design and fabricate nanoscale neural elements using emerging semiconductors, such as 2D materials, ferroelectric polymers, and hybrid organic–inorganic systems, exploring their potential for in-memory sensing, learning, and computation. By integrating these materials into scalable device arrays, the project aims to create neuromorphic systems capable of energy-efficient information processing.
Working within the SustAI CDT’s multidisciplinary environment, you will collaborate with researchers across electronics, photonics, and machine learning to advance the next generation of green intelligence technologies—bridging materials innovation and sustainable AI architectures. You will join the multi-disciplinary Flexible Nanoelectronics Lab, work at the world-class labs of the Optoelectronics Research Centre, while you will have the opportunity to build connections with UK and European research partners by being affiliated also with the UK Multidisciplinary Centre for Neuromorphic Computing.
Deadline : 17 Jul 2026
(25) PhD Positions – Fully Funded
PhD position summary/title: Flexible Hybrid thermoelectric materials and devices
Wearable electronics such as smart watches, smart glasses or smart pacemakers, have been hailed as the next generation of mobile electronic gadgets that can transform our daily lives. Despite the explosive growth of wearable technology the majority of wearable devices are still powered by batteries that require frequent recharging and replacement even though these devices require energy autonomy for an extended service time without the user’s intervention.
A possible solution for the realization of self-powered wearable devices is the generation of power from body heat using flexible thermoelectric (TE) generators. TE devices have the ability to convert heat directly into useful electricity based on the Seebeck effect. TE devices have many advantages such as solid-state operation with no moving parts, zero-emission, silent operation, vast scalability and high reliability with no maintenance and long operating lifetimes.
Despite these merits there are a number of drawbacks of existing TE generators which include low efficiency, large size, brittleness and inflexibility as they are fabricated onto rigid substrates. Researchers have demonstrated harvesting sufficient energy from body heat to power a wireless ECG system using such modules but the lack of flexibility means it is not a practical solution.
Deadline : 1 Apr 2026
(26) PhD Positions – Fully Funded
PhD position summary/title: Development of advanced intelligent systems for enhancing reliability and mission assurance of small satellites
Small satellites are revolutionising space access by enabling fast, low-cost development. However, their risk-tolerant nature often leads to mission failures, especially in early generations. Unlike traditional satellites, small satellite teams are small, with a low level of experience, and their risk assessment practices are rarely shared across institutions or countries, limiting collective learning and improvement.
Using publicly available data from over 2,500 past small satellite missions, you’ll apply big data analysis and natural language processing to extract and analyse on-orbit malfunctions. AI tools will overcome language barriers, enabling access to global datasets, including non-English sources. The result will be a comprehensive malfunction database and a risk assessment tool that prioritises risks based on mission type and team experience. It will also propose cost-effective countermeasures, allowing future small satellite developers worldwide to make informed decisions regardless of location or language.
Deadline : 30 Jun 2026
(27) PhD Positions – Fully Funded
PhD position summary/title: AI for circular economy, policy design and industrial collaboration
This PhD explores how AI tools and research techniques, such as algorithmic game theory, AI-supported mechanism design, and agent-based simulation, can help evaluate industrial collaboration opportunities and policy incentives in the circular economy. Working across disciplines, you’ll develop decision-support tools to assess B2B synergies, design smart contracts, and simulate policy outcomes for more sustainable economic transitions.
Deadline :27 Jan 2026
(28) PhD Positions – Fully Funded
PhD position summary/title: Coupling nuclear spin dynamics to mechanical motion
At the centre of this project is an experiment to explore gravity in a new parameter regime. The future goal is to realise a proposed protocol to probe gravity as a witness of quantum entanglement. Two masses are prepared in quantum superposition states each and only gravity interaction is allowed between the two particles. If gravity has any quantum aspect, it will be able to quantum entangle the two masses which will be probed in the proposed experiment, see Bose 2017 for the full protocol.
The key technology for generating large enough superpositions is to map a spin superposition to the centre of mass motion of each particle. The nuclear spin has the unique feature to remain coherent for much longer than electron spins or any other particle’s internal quantum states. A levitated silica nanoparticle will be trapped optically in vacuum. It contains a rare isotope of silicon with a nuclear Spin ½ state which can be routinely manipulated by common nuclear magnetic resonance (NMR) techniques.
For instance, superpositions of Spin-UP and Spin-DOWN can be generated and remain coherent for seconds. Magnetic fields, magnetic field gradients, and radio-frequency pulses Can be used to couple this spin superposition to the centre of mass translation motion or rotation of the levitated particle by techniques known as magnetic resonance force microscopy (MRFM). The extreme force sensitivity of levitated particles allows to benefit form few or even single Spin states to prepare the motional state.
Deadline : 15 Jan 2026
(29) PhD Positions – Fully Funded
PhD position summary/title: Quantum gravity across fundamental and emergent systems
At the centre of this project is an experiment to explore gravity in a new parameter regime. A levitated probe mass will be used to sense the gravity generated by a rotating mass disc. The experiment will be with advanced vibration isolation and inside a 4K cryostat to reduce noises and is extending a recent two-mass experiment by reducing the source mass. The probe mass motion will be monitored and compared to theoretical models of non-equilibrium dynamics to understand all relevant processes and effects visible in the particle oscillation.
The aim is to reach the measurement of accelerations below 10-11 m/s², which allow for testing of gravity deviating from the Newtonian one in the lab. Testing gravity will be done in multiple ways:
- we will test gravity for small masses to confirm the distance scaling compared to the expected force laws
- we will test deviations from Newtonian gravity as can be described in a generic form by Yukawa-type modulation of the common 1/r² scaling
Deadline :15 Jan 2026
(30) PhD Positions – Fully Funded
PhD position summary/title: Energy efficient optical network-on-chip systems
The main research will focus on developing innovative solutions for energy-efficient Optical Networks-on-Chip (ONoC) systems to tackle the challenges faced by modern optical communication technologies. Silicon photonics (SiP) has become a crucial technology to overcome the limitations of traditional electrical interconnects in terms of bandwidth, latency, and energy efficiency. With the growing global demand for IP traffic, these challenges highlight the need for advanced communication systems. Your research will involve exploring advanced wavelength routing topologies and studying cutting-edge modulation techniques for devices.
Deadline : 18 Feb 2026
About University of Southampton, England – Official Website
The University of Southampton (abbreviated as Soton in post-nominal letters) is a public research university in Southampton, England. Southampton is a founding member of the Russell Group of research-intensive universities in the United Kingdom, and ranked in the top 100 universities in the world.
The university has seven campuses. The main campus is located in the Highfield area of Southampton and is supplemented by four other campuses within the city: Avenue Campus housing the School of Humanities, the National Oceanography Centre housing courses in Ocean and Earth Sciences, Southampton General Hospital offering courses in Medicine and Health Sciences, and Boldrewood Campus housing an engineering and maritime technology campus and Lloyd’s Register. In addition, the university operates a School of Art based in nearby Winchester and an international branch in Malaysia offering courses in Engineering. Each campus is equipped with its own library facilities. The annual income of the institution for 2021–22 was £666.8 million of which £114 million was from research grants and contracts, with an expenditure of £733.7 million.
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