Category: Thèse

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

There are 2 new PhD vacancies in the field of speech- and text
anonymization in the medical domain in Berlin, Germany.
 
Both positions are in the “Medinym” project, where the department of Speech
and Language Technologies of German Research Center for Artificial
Intelligence works jointly with the Quality and Usability Labs of Berlin
Institute of Technology.
 
In both departments, we’re looking for a Researcher or Junior Researcher
level, offer a 2 years contract with optional prolongation and PhD
perspective.
 
*Application deadline: Dec 23, 2022*
 
 
More details and links:
 
DFKI:
https://jobs.dfki.de/en/internal/vacancy/en-researcher-m-w-d-in-506968.html
 
TU Berlin: https://tub.stellenticket.de/de/offers/157309/?locale=en
 

 
Please circulate upon potentially interested. In case of questions pls
contact me, I’m happy to help and forward you with respect to both
vacancies.

The Aalto Robot Learning research group operates in the intersection of
artificial intelligence and robotics. We focus on developing methods for
reinforcement learning, robotic manipulation, decision making under partial
observability, imitation learning, and decision making in multi-agent
systems. For more information, please see
https://www.aalto.fi/en/department-of-electrical-engineering-and-automation/robot-learning.
 
The Robot Learning research group is seeking a talented PhD student to
develop novel reinforcement learning and planning methods. The methods will
be used for automating hardware acceleration generation. The project is a
collaboration with the hardware accelerator design group at Tampere
University. The project aims to create a novel automated accelerator
architecture generation tool based on reinforcement learning that starts
from high-level program descriptions and generates complete compiler
programmable accelerators with desired qualities such as optimized
energy-efficiency within a set computational latency.
 
The developed methods may be based on one of our focus areas including, but
not limited to, reinforcement learning and planning under uncertainty. The
exact direction of the research is chosen depending on your experience and
interests. Please relate clearly to some of the research topics in your
Letter of Motivation.
 
Outstanding researchers from the areas of Machine Learning, AI, and
Robotics and related areas including Reinforcement Learning, Control
Engineering, Computer Vision, Statistics & Optimization, or Mathematics &
Physics are welcome to apply. The candidate is expected to conduct
independent research and at the same time contribute to the topics listed
above. Successful candidates will be furthermore given the opportunity to
work with undergraduate, and M.Sc. students.
 
The research group collaborates with research groups in hardware design,
construction, computer vision, robotics, mobile heavy machines, human-robot
interaction, reinforcement learning, imitation learning, multi-agent
systems, under-water vehicles, and robot motion planning. Moreover, one of
the PhD students in the research group is located at the Intelligent
Autonomous Systems institute at TU Darmstadt, Germany, so there will be
ample opportunities for international collaboration.
 
WE OFFER
 
The position will be filled for a period of 4 years (2 + 2). The starting
date is in January 2023 or as mutually agreed. The salary will be based on
both the job requirements and the employee’s personal performance in
accordance with the salary system of Finnish universities. The starting
salary for a PhD student is approximately 2600 EUR/month.
 
We offer a wide range of staff benefits, such as occupational health care,
flexible working hours, excellent sports facilities on campus and several
restaurants and cafés on campus with staff discounts. The position is
located at the Aalto University Otaniemi campus which can be easily reached
by public transport.
 
HOW TO APPLY
 
Please submit your application through our online recruitment system at
https://www.aalto.fi/en/open-positions/phd-student-position-in-reinforcement-learning-and-planning-for-hardware-accelerator-design
 
The closing date for applications is December 18th, 2022 (23:59 EEST
(GMT+3)).
Please write your application and all the accompanying documentation in
English and attach them in PDF format. Please attach only the following
documents to your application:
* A letter of motivation describing your research interests and how the
research fits to the Robot Learning research group (max. 1 page)
* Curriculum vitae (including the contact details of two referees)
* A list of publications as a part of the curriculum vitae
* PDF copy of your MSc and BSc degree certificates, including transcripts
of all MSc and BSc university records (grades and courses) and their
English translations (Finnish and Swedish certificates are also accepted)
 
Please note that our recruitment system allows max 5 attachments, so please
combine the copies of certificates and transcripts in one PDF, if necessary.
 
ADDITIONAL INFORMATION
 
For further information, please contact Assistant Professor Joni Pajarinen,
joni.pajarinen@aalto.fi. Additional information in recruitment process
related questions, please contact HR Coordinator Camilla Hanganpää,
camilla.hanganpaa@aalto.fi.
 
ABOUT AALTO UNIVERSITY, HELSINKI, AND FINLAND
 
Aalto University (aalto.fi) is located in Finland. Finland is among the
best countries in the world according to many quality of life indicators,
including being the happiest country in the world (UN study 2018 and The
World Happiness Report 2022). In addition to a computing cluster at Aalto
University and computing clusters in Finland available to the research
group, Finland has Europe’s fastest supercomputer LUMI. Aalto University is
the foremost university in Finland in Engineering, Design and Business.
Less than a 15 minutes metro ride away from center of Helsinki, capital of
Finland, Aalto offers access to rich cultural and social life to help
maintain healthy work-life balance.
 
Aalto University is a community of bold thinkers where science and art meet
technology and business. We are committed to identifying and solving grand
societal challenges and building an innovative future. Aalto has six
schools with nearly 11 000 students and a staff of more than 4000, of which
400 are professors. Our main campus is located in the Helsinki metropolitan
area, Finland. Diversity is part of who we are, and we actively work to
ensure our community’s diversity and inclusiveness in the future as well.
This is why we warmly encourage qualified candidates from all backgrounds
to join our community.

*#Call for #applications – #Research #Fellowship at Università degli Studi
di Bari, Bari (ITALY)*
 
Closing date for applications: 09/Dec/2022
Keywords: #artificialintelligence, #deeplearning, #healthcare, #wellbeing.
 
URL for applications:
https://reclutamento.ict.uniba.it/assegni-di-ricerca/concorsi/2022-pr-01.82
 
Applications are invited for a 18-month fellowship (assegno di ricerca) for
conducting research within the project “Artificial intelligence techniques
for monitoring adverse reactions of anti-covid vaccines”, funded by POC
PUGLIA FESR-FSE 2014/2020- azione 10.4 “Interventi volti a promuovere la
ricerca e per l’Istruzione Universitaria” –“RIPARTI” (Regione Puglia). The
Research Fellow will work on Artificial intelligence techniques for
monitoring adverse reactions of anti-covid vaccines. The Research Fellow
will spend 10 months in Miraclesrl (https://lnkd.in/dT84mYzt) and 8 months
in DIB – Dipartimento di Informatica – UniBa.
 
For further information, please contact prof. Giovanna Castellano:
giovanna.castellano@uniba.it
 
########################################################
*PhD scholarship for graduates in Computer Science or Mathematics*, on
‘Explainable computational methods for the analysis of bioinformatics data
produced by high-throughput CRISPR screening’, to be carried out at the
Institute of Biomedical Technologies of the CNR and the Mathematical or
Computer Science Department, in Bari (Italy).
 
Knowledge in programming and data analysis is required, while
bioinformatics skills will be acquired during the PhD.
 
Call for applications: https://lnkd.in/diDYsV_7 (page 41 ITA version and
page 36 ENG version) ID code CN00000041, CUP H93C22000430007
 
Deadline for application: December 19, 2022
 
Please contact arianna.consiglio@cnr.it if interested
 
Abstract:
CRISPR/Cas9 technology allows cutting a target region on DNA to perform
specific changes to a cell’s genome. The versatility of this technique has
allowed the development of countless applications in Biomedicine and opens
up new scenarios in the treatment of complex pathologies such as genetic
diseases and diseases with multiple DNA recombinations, such as tumors. The
large amount of data produced while testing the efficacy of this technology
requires the definition of innovative analysis methods that provide
explainable predictions regarding the possible effects of genomic
modifications on cellular systems.
The project will include the study of possible genetic modifications which,
induced in pathological cells, can restore their physiological activities.
In particular, aspects such as the feasibility of applying the
modification, the efficiency of its introduction into the cellular model,
the positive and negative effects that the genetic modification can cause
in the system will be predicted and evaluated.

Our Deep NeuroCognition Lab in NTU and A*STAR, Singapore is currently
recruiting multiple PhD students and postdocs. Research experiences in
neuroscience, cognitive science and AI are preferred.
 
Students and staff members will receive competitive monthly
salaries/scholarships and other benefits (e.g. medical insurance, annual
leave, sick leave).
 
Join us: https://a0091624.wixsite.com/deepneurocognition-1/join-us
 
If you are interested in applying,
send your CV + research statement to Mengmi Zhang (a0091624@gmail.com).
 

We are recruiting a PhD student to work on structured generative
models of images and videos, in the School of Computing Science at the
University of Glasgow.

Possible research topics include:
– deep generative models of images representing their latent 3D structure
– structured models for video incorporating physical constraints
– object-centric and compositional generative models

You should have a good degree in computer science / maths / similar,
and excellent coding skills. Funding is available for strong
candidates; there is a deadline for scholarships at the end of
January.

Please contact me directly (via paul.henderson@glasgow.ac.uk) before
applying, to discuss suitability and negotiate a research topic.
Include your CV, transcripts, and a short (one page) research
proposal.

The University of Glasgow (est. 1451) is a member of the UK’s Russell
Group of leading research universities. It is ranked 5th in the U.K.
for computer science [Times Good University Guide 2022]. The School of
Computing Science has over 70 faculty, and over 130 research students.
The School particularly encourages applications from groups who are
traditionally under-represented in computer science.

Best regards,

Dr. Paul Henderson

Assistant Professor (Lecturer)
School of Computing Science
University of Glasgow
https://www.pmh47.net

Dr. Patrick Shafto’s lab in the Department of Math and Computer Science at
Rutgers University in Newark will be accepting applications for PhD
positions starting Fall 2023. Positions are fully funded and include a
stipend.
 
The PhD program is a unique combination of training in pure mathematics
with research in machine learning. Graduates of the lab have gone on to
positions in industry and academia.
 
Dr. Shafto’s Cognitive and Data Sciences lab studies machine learning and
human learning with tools from probabilistic machine learning, pure
mathematics, and behavioral experiments. More information about the
research can be found at http://shaftolab.com/. Recent papers and preprints
represent current research directions.
 
Interested applicants should reach out to Dr. Shafto describing their
research interests and experience. Application information should be
submitted to the Mathematical Sciences PhD program through the Rutgers
website.

The Computational Neurophysics lab at the University of Bremen headed by
Dr. Udo Ernst offers – under the condition of job release – at the earliest
possible date a position until 30.09.2024:
 
 
*Research Assistant in Computational Neuroscience (f/m/d)*
 
German federal pay scale E13 TV-L (65 %)
 
for the research project
 
 
*I-See2 – Improving Intracortical Visual Prostheses Using Complex Coding
and Spontaneous Activation States*
 
 
The lives of many individuals are strongly impaired by missing the sense of
vision. One putative option to help them are cortical prostheses. Such
devices record images from our environment, encode this information into
the ‘language’ of the brain, and stimulate neuron populations in the visual
system for creating a corresponding percept.
 
 
I-See2 (www.isee.uni-bremen.de) investigates new principles for building
cortical prostheses in an international group with researchers from Canada,
Switzerland and Germany. Your position will be at the heart of the project
in Bremen, where computational methods are developed and data from our
partner labs comes together. You will perform modelling of visual
information processing, analysis of data from electrophysiological
multielectrode recordings and optical voltage imaging, and be involved in
model-driven visual stimulus generation for animal and human experiments.
The position comes with direct supervision by the principal investigators
and project coordinators Dr. David Rotermund and Dr. Udo Ernst.
 
 
*Requirements:*
 
Ideal candidates have a master’s degree in Computational Neurosciences,
Physics, Computer Sciences or in related fields. They must have a strong
background in neural networks, dynamical systems, mathematics and/or data
analysis, and be experienced in programming (preferably in Python/Matlab).
Above all, they must have a strong motivation, a sense for responsibility,
a distinct desire to learn, and the ability to proactively collaborate in
an international research environment. Fluency in English is required (both
written and spoken).
 
 
The Computational Neurophysics Lab offers a good working atmosphere, direct
involvement in international research and attractive facilities.
 
The university is family-friendly, diverse and sees itself as an
international university. We therefore welcome all applicants regardless of
gender, nationality, ethnic and social origin, religion/belief, disability,
age, sexual orientation and identity.
 
As the University of Bremen intends to increase the proportion of female
employees in science, women are particularly encouraged to apply. In case
of equal personal aptitudes and qualification priority will be given to
disabled persons.
 
Please explicitly address each of the specified requirements in the
application. Detailed instructions are described below. Please send your
application documents (motivation letter, CV, copy of your degree
certificates including high school, list of skills, awards, publications,
contact details of two academic reference) until December 9th, 2022 by
indicating the job id A325/22 to:
 
University of Bremen FB1
Computational Neurophysics lab
Secretary of Dr. Udo Ernst
Mrs Agnes Janßen
Hochschulring 18, Cognium
D-28359 Bremen
Tel.: +49 421 218 62000
 
or in electronic form in a summarized PDF file via e-mail:
ajanssen@neuro.uni-bremen.de.
 
 
For questions of the research project please contact:
 
Dr. Udo Ernst, E-Mail: udo@neuro.uni-bremen.de
 
 
See also for general information on this topic:
 
http://www.isee.uni-bremen.de
 
 
*Detailed instructions for applicants*
 
Your application must comprise:
 
 
*Motivation letter*
 
Your 1-2 page essay should reply the following questions:
 
• What is your background? In which fields have you worked before and how
do you think this can be useful for the present job?
 
• What attracts you to the field of computational neuroscience?
 
• Which problem(s) in computational neuroscience are you most interested in?
 
• What is your motivation to join our project?
 
• What are your plans for your future career?
 
 
*Curriculum Vitae*
 
Send a classical tabular CV with your contact details, and all stages of
education and employment.
 
 
*List of skills, awards, publications*
 
List your skills, especially proficiency in languages (including the level
of proficiency), that you think might be useful for the job. Also list
awards you might have got and peer-reviewed papers, in case there are some.
 
 
*Contact details of two academic references*
 
One of the references should be your MSc advisor. Please contact the
references prior to listing their names so that they are not surprised if
they get contacted.
 
Your application can be in English or German, whatever language you are
more familiar with.