Author: Maxime Sangnier

Follow this link for descriptions of PhD Studentships at the University of
East Anglia – deadline for application 22nd February 2023:
 
https://www.uea.ac.uk/web/research/research-with-us/postgraduate-research/latest-phds-and-research-studentships
 
A number of these projects have substantial computational modelling (e.g.
supervisors: Vaghi, Smith, Penny) and brain imaging (e.g. supervisors:
Vaghi, Smith, Penny, Renoult, Holmes, Coventry, Gliga, Coventry)
components.
 
Please address any queries to the relevant supervisor.
 
All the best,
 
Will Penny,
School of Psychology,
UEA.

I’m pleased to have been awarded a Queen Mary University of London Principal’s Scholarship to offer to a top quality, UK-qualified candidate to study with me and a colleague in Mathematics (Primoz Skraba).
 
We’re looking for someone who is comfortable with mathematics and also knows some combination of acoustics, audio and music.
 
This project – under the banner of Artificial Neuroscience – will look at an emergent approach to Deep Learning that applies Linear Algebra and topology to weight and activation matrices in Neural Networks. This will be studied in the context of Neural Audio topics like Music Source Separation and Enhancement, Audio Temporal-Structure Decomposition (chorus/verse, etc) and Audio Synthesis.
 
Here is a link to our web site which candidates can follow to make an application. Closing date is 31 January 2023. http://eecs.qmul.ac.uk/phd/phd-studentships/ The project description can be found here: scroll down and click on *Artificial Neuroscience*. http://eecs.qmul.ac.uk/phd/phd-studentships/principal-and-epsrc-dtp-phd-studentships-in-electronic-engineering-and-computer-science/

*Ph.D. position at University of Texas at Dallas, Data Security and Privacy
Lab*
*DEADLINE: January 1st, 2023*
 
The University of Texas at Dallas (UTD) is a rising research powerhouse
with eight schools and more than 140 academic degrees including
top-ranked programs in business, engineering, science, audiology and
arts and technology.
 
At UTD Data Security and Privacy Lab, we focus on creating technologies
that can efficiently extract useful information from any data without
sacrificing privacy or security. We have been working on security and
privacy issues raised by machine learning and AI, privacy
issues in social networks, security issues in databases, privacy issues
in health care, applied cryptography for data security, risk and
incentive issues in assured information sharing, use of data mining and
machine learning for fraud detection, botnet detection and homeland
security. Recently, we have been focusing on security, accountability
and privacy issues in machine learning, secure IoT data processing and
using blockchains for assured data sharing, and graph neural networks for
cybersecurity.
Please see a high level interview with lab director *Dr. Murat Kantarcioglu* on
the lab’s research directions.
 
https://research.utdallas.edu/blog/qa-with-dr-kantarcioglu
 
For the ongoing projects at the lab, we are looking for exceptional
Ph.D. students to join our team. Ideal candidates should have strong
knowledge in databases, data mining, machine learning and cybersecurity.
Excellent programming skills are also required. We are aware of the fact
that knowing all the subjects listed above is impossible for an
undergrad student and even very difficult for a master student.
Therefore, highly motivated students who know some of the above subjects
listed above are encouraged to apply. If you are interested in joining
our lab as Ph.D. student, please submit your CV, unofficial transcript and
additional information via the next form.
 
https://docs.google.com/forms/d/e/1FAIpQLSeTds_vdzv_Dw_fynOCEt3lOK_52IVOt-XDMlJVNdCfm29Ddw/viewform?usp=sf_link

*Ph.D. – **RESEARCH ASSOCIATE** (m/f/d)*
 
Full time, for a maximum of* 4 *years*. *The remuneration is in accordance
with *TV-L 13
<https://www.tuhh.de/t3resources/tuhh/img/universitaet/service/verwaltung/PV32/Entgelttabelle.pdf>.*
 
YOUR TASKS
 
 
– Fundamental research on topics related to the Institute for Data
Science Foundations
– Publication of research results in journals and at relevant computer
science / mathematics conferences
– Support of teaching activities and knowledge sharing tasks at the
Institute for Data Science Foundations

 

YOUR PROFILE
 
– Completed scientific university studies, in particular in the subject
area/s mathematics, computer science, or comparable
– Very good communication skills in English for publications, lectures
and project meetings
– Knowledge in relation to some of the following areas is an advantage:
o Data science
o Artificial intelligence
o Robotics o Machine learning (supervised learning, deep neural
networks, statistical learning theory, reinforcement learning)
o Mathematical background, for instance in: probability theory,
functional analysis, differential and/or algebraic geometry, information
theory, information geometry
– The following knowledge is an advantage:
o Experience in scientific writing
o Experience in Python programming

OUR OFFER
 
– We offer you the opportunity for scientific qualification with the aim
of a doctorate
– Become a member of the Institute for Data Science Foundations, and
benefit from excellent supervision and interdisciplinary exchange
– Integrate into an excellent research network with national and
international opportunities for collaboration
– Work in a motivated and interdisciplinary team, and develop your soft
skills

 

 

For further information please contact Prof. Dr. Nihat Ay, e-mail:
nihat.ay@tuhh.de or his assistant Ms. Sandra Krüger, e-mail:
sandra.krueger@tuhh.de.
 
We particularly encourage women to apply. Due to their underrepresentation,
they will be given priority in cases of equal suitability, qualifications
and professional performance.
 
Please send your complete application documents (cover letter, curriculum
vitae in table form, proof of completed training and/or university degree,
job references or certificates of employment) via the online application
system.
 
Notice for graduates of foreign educational qualifications: Please submit
proof of all obtained university degrees and, if available, the recognition
of your educational qualifications in Germany (e.g. anabin excerpts and/or
acknowledgement of previous employers).
 
We look forward to receiving your online application by* December 31st
2022.*
 
 
Position Description
<https://stellenportal.tuhh.de/jobposting/f9366b962b061a0b235d35bafd63eacccf5b0f430>
 
Please upload your application documents here
<https://stellenportal.tuhh.de/de/jobposting/f9366b962b061a0b235d35bafd63eacccf5b0f430/apply>
.
 
 
Kind regards
 
*Sandra Krüger*
 
*Assistant to Prof. Dr. **Nihat Ay*
 
*Institute for Data Science Foundations // E-21*
Hamburg University of Technology // TUHH
Hamburg Innovation Port // HIP ONE
 
Blohmstraße 15
5th floor
21079 Hamburg

Plant diseases may affect the root, steam and leaves of plants resulting in
a sizable drop of revenue for farmers as crop’s quality is affected and may
lead to food shortage and food chain disruption [1]. Traditionally, a crop
disease can be detected by visual inspection which can be a tedious
enterprise which is time and effort consuming, and errors prone. Farming
has developed extensively in the last few decades taking advantages from
developments in chemistry, physics, sensing technology, data processing and
analytics, artificial intelligence and IoT [1,3-4]. The demand for mobile
portable applications in agriculture has increased as portable technology
ubiquitousness allows for a wider deployment and a better
cost-effectiveness. With the technology, farmers can identify and detect
early infections and diseases and hence mitigate their impact, improve
treatments outcome and can prevent further infections from re-occurring.
Portable spectroscopy can be used to detect the presence of diseases on
leaves and categorise healthy plants from unhealthy ones. Such a technology
has found use in many agro-food applications as it offers short processing
times, cost-effectiveness, portability and ease-of-deployment [2,5].

Spectroscopy is the analysis of matter and its interaction with
electromagnetic radiations; and a spectral signature is the variation of
reflectance or emittance of a material with respect to wavelengths. It is a
non-destructive way to find the fingerprints of components; and hence is a
suitable method to inspect plants’ samples.

Reflectance is a measure of electromagnetic energy that bounces back from
the surface of a material; and the leaf reflectance in the visible and
near-infrared ranges are influenced by a variety of interactions (including
leaf surface and water content) which can lead to a suitable use in
classification and detection. Further, green vegetation spectral signatures
can show pigmentation in plant tissues as Chlorophyll growth is affected.
Hence it can be used for anomaly detection in remote sensing applications.
Counting the number of insects of various species is important for planning
pest control, and for guiding agricultural policy. Computer vision
algorithms can be trained with the captured footage to detect the soil
conditions, analyse the aerial view of the overall agricultural land, and
assess crop health information. Computer vision-enabled machines can be
used in sorting and grading the harvest; while automating such tasks can
offer efficiency [2,3].

Hyperspectral imaging in agriculture can significantly extend the range of
farming issues that can be addressed using remote sensing. Almost every
farming issue (weeds, diseases, etc.) changes the physiology of plants, and
therefore affects its reflective properties. Healthy and unhealthy crops
reflect the sun light differently which renders it possible to detect such
changes in the physiology of the plants and correlate them with spectra of
reflected light.

Hence the objectives of this research proposal are:

To address the complexity of crop disease monitoring and detection in the
context of smart farming taking account of different data types.

To develop a solution that integrates both computer vision and spectroscopy
related information.

To design an AI based system for classification of diseases and anomaly
detections.
Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class
Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent
qualifications, for example, a Lower Second Class Honours Degree plus a
Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of
evidence from applicants who have appropriate professional experience which
is equivalent to the learning outcomes of an Honours degree in lieu of
academic qualifications.
Desirable Criteria

If the University receives a large number of applicants for the project,
the following desirable criteria may be applied to shortlist applicants for
interview.

– First Class Honours (1st) Degree
– Masters at 70%
– Experience using research methods or other approaches relevant to the
subject domain
– Work experience relevant to the proposed project
– Publications – peer-reviewed


for further information, please
visit https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1455586