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PHD POSITIE BIOMARKER DISCOVERY FOR PRESYNAPTIC DEGENERATION IN NEURODEGENERATIVE DISEASES

Mol
PhD
Description: 

Alzheimer’s disease (AD) is the most common neurodegenerative disease affecting approximately 35 million people worldwide. To date, the only available treatments address symptoms and have no effect on the disease progression. As there is overwhelming evidence that there is lengthy period prior to symptom development, there is an increasing need for novel biomarkers with increased sensitivity that can facilitate earlier disease diagnosis, disease progression and treatment response. Promising candidate biomarkers include SNAP-25, GAP-43, LAMP, NP1 and Synaptotagmin-1. However, these protein markers need to be validated in larger patient’s cohorts using more sensitive and specific methodology. In addition, exosomes have been shown to play an important role in AD progression and therefore further research is required to fully explore their potential role in pathological transmission between connected brain areas. Exosomes are small nanosized vesicles that are secreted by most cells in the CNS and they carry various molecules such as like lipids, proteins and RNA. In this project, we aim to validate the candidate biomarkers in a larger patient cohorts in addition to isolating and characterizing exosomes derived from AD patient CSF/plasma samples. The molecular content of exosomes may not only deliver novel biomarkers, but could also provide new insights in the development and progression of AD.

The project is a collaboration between VITO, University of Antwerp and 3 industrial partners. The experimental works will be conducted at different locations (mainly Mol, Antwerp and Ghent), so good planning skills are needed. The main technology you will use in your project is mass spectrometry based proteomics as well as advanced microfluidic separation and purification methods for exosome isolation and fractionation, so interest in analytical tools is a must. Also interest in data science and statistics is a plus.

 

Deadline for registration: 31/01/2019
Start date of the project:  1/09/2019


 

PHD POSTION DEVELOPING IN-SILICO TOOLS TO DESCRIBE AND PREDICT AFFINITY SEPARATION OF MOLECULES ON FunMenTM CERAMIC MEMBRANES

Internationaal
PhD
Description: 
As up to 90% of chemical production processes contain a separation procedure, these separations account for 40 to 70% of the global capital and operational costs incurred by the process industry. There exists a clear need for the development of cost efficient separation techniques. Intensifying chemical/biochemical separation processes will have a huge impact on cost and energy savings leading ultimately to greener manufacturing.

Recently Vito and UAntwerpen developed a new ceramic functionalization methodology, FunMemTM, that can be applied to metal oxide based ceramic membranes known for their excellent structural, thermal, physical and chemical stability explaining their broad organic solvent stability. The innovative Grignard grafting method of VITO/UA provides a stable hybrid organic-inorganic material, and allows a broad range of functionalities next to hydrophobisation.

These membranes can lead to affinity-based separations governed by the affinity of the functional groups. Particularly, molecules with very similar size can be separated based on their membrane-molecule affinity difference, and preferential transport (i.e. negative retentions) is observed for molecules with a specifically high solute-membrane affinity in organic solvent nanofiltration.

The cheminformatics team of ICOA, CNRS-Univ Orléans led by Pr. Pascal Bonnet develops and applies in silico tools to understand molecular systems and interactions involved in several areas. The research of ICOA/VITO in this project, will aim at understanding the fine interplay between solute, solvent and surface at the atomistic level by developing modelling tools. Understanding the interaction of organically modified metal oxides with the environment is key to develop predictive models that could be implemented during the design of tailor-made ceramic membranes for specific processes.

The PhD candidate will develop in silico tools to analyse and understand on a molecular basis the flow mechanism of solutes through the functionalized ceramic membrane. By using statistical models and molecular dynamics simulations, the candidate will determine which descriptors and parameters are the most relevant for affinity separation using membrane nanofiltration. Understanding, quantifying and predicting interactions makes a direct contribution to further understand and control the performance of these materials that will be used in membrane-assisted process intensification.

 

Collaboration with University of Orleans

Registration deadline: 17/12/2018 

PHD POSITION ACTIVE LEARNING FOR SOLID WASTE CHARACTERIZATION

Mol
PhD
Description: 

VITO and UGent are looking for candidates for a PhD research position on active learning for solid waste characterization.

Research question:

  • Improved training strategies: How to train a classification model for solid waste characterization by multimodal images (x-ray, 3D and color) with minimal or no training data labeling by humans.
  • Active and semi-supervised learning: How to reduce the time spent by humans on training the deep learning networks, by clever 'human-in-the-loop' strategies. This also involve research into efficient interactive tools, for visualizing and interacting with the learning process.
  • Transfer learning: how to optimally make use of older training data and/or older trained networks in retraining for a new use case. How to “teach” or to steer how the algorithm calculates particle “similarity” for unseen particle classes?
  • Low level image preprocessing to reduce the input parameter space of deep learning and hence reduce the number of required labeled training samples.

Background:

The characterization of waste has many important applications in recycling processes:

Process engineering: determine the technical and economical feasibility of waste sorting processes and design of new waste sorting processes
Online process optimization: measure, control and optimize sorting processes
Quality control: warrant the quality of recycled products to establish market trust

 

Currently, waste characterization is still often done manually. This approach is slow, subjective, expensive, unpleasant and eventually it delivers only little information. There is a need for a fast, objective and automated method that delivers data on a much more detailed level. Therefore VITO started the development of a multi-sensor characterization device and the required machine learning algorithms. Installed sensors include dual energy x-ray transmission, 3d laser triangulation and a high resolution color camera.
Currently, the algorithms have been trained to successfully recognize about a dozen of material types within mixed waste streams. Algorithm training was performed by feeding the device “pure“ material streams, of which many were manually prepared to ensure correct labeling of the data. Not surprisingly, this preparation of “pure” (mono-material) streams turned out the be a major challenge in the training process of a waste characterization device.

 
Therefore VITO wants to investigate how to develop a semi-supervised learning framework that can learn to classify waste particles with a minimum of labeled particles, while still learning from the similarities between unlabeled particles. This approach is known as active learning.
During the training process, the system should classify particles in existing classes and suggest new classes to the user. For this, interactive visualization tools should be developed that allow the user to explore and interact with the particle similarity that the algorithm calculates (e.g. t-SNE).

 

Collaboration with University of Ghent

Scientific Promotor: Prof. dr. ir. Wilfried Philips

Scientific co-promotor: Prof. Peter Veelaert

 

Registration deadline: 17/12/2018


 

PHD POSITION DEVELOPMENT OF COMBINED LIXIVIANT AND PASSIVE METAL CAPTURE SYSTEMS FOR INDUSTRIAL WASTE VALORISATION

Mol
PhD
Description: 

VITO, in collaboration with Cardiff University (UK), is looking for a motivated Ph.D. candidate with, preferentially, a Master in Chemistry, Mineral Engineering, Biochemical Engineering or Metallurgy. The proposed Ph.D.. is hosted both by VITO (Belgium), in particular the Waste Recycling Technologies team, and the School of Engineering at Cardiff University (U.K.). The student will have the opportunity to work at the state-of-the-art laboratories of both institutes.

Low metal grades and the enormous volumes of mineral waste materials do not allow for traditional metallurgical processes to process the material and recover metals. Low-cost and low-intensity technologies that can process large volumes of material are urgently needed for wastes. Whilst in situ metal leaching approaches such as heap leaching have been developed and utilised for metal recovery from primary low-grade Cu, Au and U ores the application of heap leaching technology to much more complex mineral waste materials is new and constitutes a promising research gap to be explored. The development of the heap leach technology for waste is a question of research and development of (i) appropriate (environmentally benign and effective) lixiviants which can be used in conjunction with (ii) low-intensity metal capture systems. In 2016, VITO was one of the first research institutes to report experimental research regarding heap leaching of mineral waste using lab scale column leaching tests that provide information on the leachability of metals from the material and permeability of leaching liquors through the material. As yet corresponding low-intensity metal capture systems have not been developed. Thus this PhD seeks to (i) further develop appropriate lixiviants for application to waste, and (ii) develop low-intensity metal capture systems that work with the lixiviant/waste chemistry. This is a truly ground-breaking area of research and will lead to important technical and scientific development.

Collaboration with University of Cardiff

Registration deadline: 17/12/2018

PHD POSITION ADJOINT-BASED TOPOLOGY OPTIMIZATION OF THERMAL NETWORKS

Genk
PhD
Description: 
Thermal networks are an important means to decarbonizing building heating supplies. When designing such thermal networks, several competing objectives arise. At the one hand, investment costs are decisive for the achievability of the project, while also energy-efficiency and dimensioning of pump capacity, at the other hand, are important elements in the design process. With the transition to 4th generation district heating networks, the design challenge becomes ever more challenging. That is, to achieve the best energetic efficiency, a balanced combination of heat storage, energy conversion units, and a diverse collection of (waste) heat sources at different temperatures should be pursued. In scientific literature and within our own research in the EnergyVille framework, automated design methods based on numerical optimization are therefore developed.

Using these methods the configuration (topology) of the entire network is hereby fully parametrized and optimized for the above mentioned goals, leading to a complex optimization problem with many design variables. The state-of-the-art in district heating network design therefore resorts to linearized models to optimize the network configuration. These model assumptions sincerely restrict the applicability of these methods to true 4th generation networks. Moreover, design of large-scale networks is not possible due to the exponential scaling of the computational cost with the network dimension.

Recent research in our group has shown that adjoint-based optimization techniques can provide a solution to this problem. In short, the adjoint method is an approach that enables sensitivity calculation of an objective function to large amounts of design variables at a fixed computational cost of about only two simulations. In addition, its applicability ranges well beyond linear problems and is used within our research group for the optimization of complex flow and heat transport problems. As such, it potentially enables automated large-scale thermal network design, including an accurate nonlinear network model.

The task of thel PhD candidate is to elaborate this research track and develop a tool that automatically proposes efficient and affordable thermal network designs.

Collaboration with University of Leuven
Registration deadline: 17/12/2018

PHD POSITION REDUCTION OF RETURN TEMPERATURES BY MORE EFFICIENT CONTROL OF THE SUBSTATION

Genk
PhD
Description: 

Reduction of the return temperature of substations enables a higher efficiency of the substations, but also enables a better heat usage in a complete heating network. In 4th generation heating networks, the aim is to reduce the supply temperatures radically, which mean that substations need to operate with higher efficiency. A low return temperature is a requirement to reduce the supply temperature, since the capacity of the network is proportional to the difference between both temperatures. Therefore, to be able to decrease the supply temperature while guaranteeing the network capacity, the return temperature should be minimized.

In this project the aim is to operate and control a substation as a multivariable system embedded in a larger network. Using a multi-objective optimization approach where the return temperature of the substation is one of the components in the objective function, the supply constraints and demand requirements will be jointly optimized.

The resulting operation and control scheme will consist of multiple levels, which will jointly act, thus low-level control and supervisory levels will not act independently. It can also be assumed that the substation design with respect to actuators and sensors might need to be updated.

Although most of the components in a substation are known and models are available, the parameters for these models need to be updated using a system identification scheme, which can also be used in the resulting substation concept as an online scheme with subsequent automated controller and optimized update.

 

Collaboration with the Lulea University of Technology (Sweden)

Registration deadline: 17/12/2018

 

 

PHD POSITION RISK-AWARE QUANTIFICATION AND AGGREGATION TECHNIQES FOR OPTIMAL VALORIZATION OF ENERGY FLEXIBILITY

Genk
PhD
Description: 

This PhD will considerably advance the state-of-the-art in optimal valorization and aggregation of energy flexibility by developing, integrating and exploiting novel algorithms for the dynamic quantification of uncertainty and risks regarding energy flexibility.

· In the state-of-the-art of flexibility research, emerging uncertainty is explored and sampled when modelling and optimizing flexibility, but precise definitions and quantifications are lacking.

· This uncertainty will be quantified during this PhD research, using appropriate data science techniques to calculate the risk-of-unexpected-unavailability of the flexibility that was offered earlier, and that take this risk into account when valorizing the energy flexibility in different applications and markets. The result will be that a larger scala of flexibility carriers and types can be valorized.

· The risk factors will be explored and quantified from the points-of-views of the flexibility buyer and the flexibility provider, and applied/adapted to a broad spectrum of energy generating/consuming device types. Ultimately the goals is to provide an approach that can be applied to any type of energy flexibility carriers, including those that cannot be explicitly modelled using a white box or grey box approach

· Novel optimization algorithms will be developed that maximize the reward functions for either and both points-of-view in the context of current and future energy and energy-flexibility markets.

 

Collaboration with University of Ghent
Registration deadline: 17/12/2018

 

PhD@VITO

Would you like to improve your career opportunities even more by gaining a doctorate? If so, be sure to familiarize yourself with the opportunities that VITO is able to offer you! Take a look at our PhD topic list.

VITO supports applicants wanting to do research for four years under the leadership of a university supervisor and a co-supervisor from VITO, resulting in the achievement of a doctorate and tying in with other research done at VITO.

Various options are possible:

VITO doctoral grant

At various times each year, VITO publishes a number of doctorate subjects, with the object of supporting VITO research. These subjects are selected carefully in advance, in the context of strategic collaboration with the university supervisor. As such, both the subject and the supervisor will have been established when an applicant starts his doctorate. The subjects selected for doctorates are usually very much application-oriented. View our PhD topic list to select a subject to get more information and apply. Check the PhD regulations for the acceptance criteria (degree, topic,...).

If you are selected you will be notified. From that moment on you get the chance to work out a PhD proposal, together with your promotor at VITO and your university supervisor. You will have to present your PhD proposal to the doctoral jury at VITO.

FWO-VITO research mandate

On 8th June 2009, FWO and VITO signed a protocol to fund 2 additional doctorates next to the FWO doctoral mandates that are granted annually. This procedure starts by submitting a standard application for an FWO research mandate. Places will be offered to applicants who are ranked high enough and whose research proposal is situated within a strategic VITO domain. More info

Doctorate in the context of European collaboration

If you have a particular interest in international issues, a doctorate in the context of a European project could present you with a unique opportunity. VITO participates in European networks, in which doctorates abroad are often made available.