Last Wednesday was my inaugural lecture. Thank you to all my family, friends and colleagues who came and made it such a special day; it was a real celebration. Thanks also for the people who helped organize it: Emma Hooley and Diane Levine, and also Steve Wang for use of a video camera.
The video of the lecture is now on Youtube. I am often out of frame – the camera was mounted on a tripod but nobody was operating it (except for one moment when one friend turned it slightly).
Here is another great opportunity to work with Tania Dottorini and me in an exciting AMR project
FARM WATCH: Fight AbR with Machine learning and a Wide Array of sensing TeCHnologies
The successful candidate will work closely with an interdisciplinary and international team of academics and industrial partners. The project offers a unique combination of expertise in machine learning, statistical and mathematical modelling, bioinformatics, sequencing, cloud computing, microbiology, infection control, food safety, surveillance, epidemiology.
Applicants must have, or be very close to completing, a PhD in statistics, mathematics, computer science, computational biology, engineering, physics or relevant computational fields. Candidates must have knowledge and experience in statistical modeling, machine learning and data mining methods and algorithms for processing heterogeneous, complex large-data, including sequencing, sensor and biological data.
The School of Veterinary Medicine and Science is committed to diversity and equality of opportunity. The School holds a Bronze Athena SWAN award in recognition of its commitment to equality and diversity and advance the representation of women in veterinary medicine.
This is a full time, fixed-term from 1 May 2019 to 31 December 2021. Job share arrangements may be considered.
Informal enquiries may be addressed to Dr Tania Dottorini, email: tania.dottorini@nottingham.ac.uk.Please note that applications sent directly to this Email address will not be accepted
Our University has always been a supportive, inclusive, caring and positive community. We warmly welcome those of different cultures, ethnicities and beliefs – indeed this very diversity is vital to our success, it is fundamental to our values and enriches life on campus. We welcome applications from UK, Europe and from across the globe. For more information on the support we offer our international colleagues, visit; https://www.nottingham.ac.uk/jobs/applyingfromoverseas/index2.aspx
FARM WATCH: Fight AbR with Machine learning and a Wide Array of sensing TeCHnologies
Applications are invited from research scientists for the above post in the School of Veterinary Medicine and Science to join an exciting project dealing with antimicrobial/antibacterial resistance (ABR).
The successful candidate will work closely with an interdisciplinary and international team of academics and industrial partners. The project offers a unique combination of expertise in machine learning, statistical and mathematical modelling, bioinformatics, sequencing, cloud computing, microbiology, infection control, food safety, surveillance, epidemiology.
Applicants must have, or be very close to completing, a PhD in computational biology, computer science, mathematics, statistics, engineering, physics, or relevant fields. The candidate must have knowledge and experience in bioinformatics techniques and approaches, particularly related to genome biology and sequence analysis e.g. (genome assembly, sequence mapping, metagenomics, transcriptomics, and annotation).
The School of Veterinary Medicine and Science is committed to diversity and equality of opportunity. The School holds a Bronze Athena SWAN award in recognition of its commitment to equality and diversity and advance the representation of women in veterinary medicine.
This is a full time, fixed-term post from 1 May 2019 to 30 June 2021. Job share arrangements may be considered.
Informal enquiries may be addressed to Dr Tania Dottorini, email tania.dottorini@nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted.
Our University has always been a supportive, inclusive, caring and positive community. We warmly welcome those of different cultures, ethnicities and beliefs – indeed this very diversity is vital to our success, it is fundamental to our values and enriches life on campus. We welcome applications from UK, Europe and from across the globe. For more information on the support we offer our international colleagues, visit; https://www.nottingham.ac.uk/jobs/applyingfromoverseas/index2.aspx
Following on from my previous post, the exercise of reading papers outside of my main discipline, and trying to rate them for originality, significance and rigour, has led me to think deeply about how to write great papers. There is now one thing I will do differently in the papers I (or rather, my group) write, which is to state clearly what the significance of the results are. I will come back to that later in the post.
More generally, the obvious part of writing great papers is to do great research. But whether the research is truly great, or merely very good, you still need to write a great (or very good paper): great research badly written risks not being recognized.
What I have noticed is that we (the academic community) are can be very good at presenting our rigour, can be a bit mixed at presenting the novelty, but are often less good at explaining the significance – we rather tend to leave that to our readers. Underlying this is the presumption that we are writing for experts in our field. But the REF means that we need to write for a broader audience – yes, we need to write for experts, but some parts of the paper (especially in the Abstract and Conclusions, but also, as I will argue, how the Results and Discussion are structured), can really help non-experts understand what your paper is about, along with its originality and significance, without compromising its rigour.
The way I encourage my group members to write papers is to identify the key results – key messages – as simple bullet points that can be easily understood. These become the main subheadings of the Results section: clear and informative. So, for example, instead of a subheading “Results of Analysis X”, a better subheading might be “Protein A interacts with Protein B” (or whatever the result happens to be). I then ask my students/postdocs to provide the evidence in support of the messages – these become the figures/tables; next I ask what scientific questions are answered by these results – these become the questions in the Introduction, and motivate the literature review in the introduction.
Here is what I will now do differently (and encourage others to do the same): for each of the main results, I will ask the question: what is the significance/importance of this result – expressed as a single sentence. The resulting sentences can then be used to structure the Discussion and Conclusions, as well as form the final sentence(s) of the Abstract. The important point is you tell the non-expert reader what the significance of the results are – this is essential for REF panels, who might then score the paper better for significance – but also improves the paper, without compromising its integrity. After all, for example, not many people might care that “Protein A interacts with Protein B” but if “therefore we could use Protein A as a drug target for disease Z” (or whatever the significance of the result happens to be) the importance is explained, and a wider community might benefit from your result.
Dov Stekel's Laboratory 