I’m a student and I’ve been visiting a collaborator of my advisor to work on a project. The visiting period ends in a few days and it has been productive and enjoyable. We have worked well together, but the professor has no shortage of students or collaborators.

What is a more “refined and professional” way (for lack of a better term) to phrase the following message:

Keep me in the loop in case you have some other projects I may be able to contribute to.

I’m a recently admitted in a masters program in mathematics. My question is about “strategy” of learning. Should I focus on learning many things or should I pick an research area and start learning only stuff about that area? This is a problem because some scholars told me that if I want to get hired in academia, I must start publishing as early as possible so I better start to deepen my knowledge in certain area. However, actually I have not decided with area I fit the most, and I think that broadness is useful as well, because diversity is a necesity for creativity, just as professional musicians doesn’t just hear say classical music, but rock music, or latin music to get inspiration, I think that mathematicians must not just read about their own area, but related areas as well.

In your experience, which “strategy of learning” is better for a begginer in graduate school? I must say that I have freedom for choose courses.

This question already has an answer here:

I did my undergraduate degree at a top University then went straight on to do a Ph.D at a pretty rubbish institution. Can this affect how my academic career could progress?

Would employers also take into account the institution where a Ph.D was gained?

I very recently learned that I will be awarded an NSF postdoc!
And then the US government shut-down.

Should I expect the NSF postdoc to work out once the government starts running again?

Note: The email I received from the NSF is not an official award notice. From the NSF website is says that during this shut-down period “no new grants or cooperative agreements will be awarded”. It does not say anything about whether grants that have been scheduled will be awarded once operations resume. Perhaps I am overthinking it, but I am worried that the NSF postdoc might fall through and it will be to late for me to make alternate arrangements.

I would like to ask my former professor for a reference, I’m not sure how to formulate it and am worried the professor won’t remember me. I submitted a good essay in her class though (got an A).

Can I write:

Dear Professor,

I studied with you 2 years ago, I don’t know if you
remember. In any case, I submitted a good essay in your class and
wanted to ask you for a letter of recommendation?

I am studying grade 11.I have a problem.Whenever I learn a new thing in science or maths (For example electronegativity, square root), I am going too deep into the subject with the internet.I have the eager to learn the whole chapter(Sometimes until A/l or university), and I can’t control myself.Even when I see something on the internet I want to learn about it completely.

By studying like this it is very hard for me to concentrate on other subjects(Sometimes I don’t have the time to study other chapters.)

I also love to code, do electronics, study calculus, do sports; and I am going to a great extent in the fields.I can’t control myself.Please give me some advice.

THANK YOU. If you think that this question would belong to another StackExchange, please let me know.

It is becoming more and more obvious that the present subsymbolic/neural networks/statistics based Artificial Intelligence (AI) research and applications experiences natural end in its development. E.g. https://futurism.com/what-would-take-home-robots-good-what-they-do/ summarizes the sad state of intelligent home robots – they are incapable of functioning despite the years of research by the most capable and most supported researchers and billions of investment by the numerous top companies. The chatbots and artificial dialogue systems are silly and are not usable. The natural language inference is only marginally developed. Yes, there are newspaper headlines about discoveries, but all of them are no scalable to the self-aware, conscious and fully autonomous intelligence. The situation is sad. And it can be attributed to the used methods – neural networks, subsymbolic-black box and statistical approaches. Logical and symbolic methods are neglected, also they hold the promise of the completion and achievement of the AI promises.

One should put this dire state of AI development in the current context – banks and investment advisors are almost unanimously in agreement that the next big economic crisis (that is imminent in the capitalist cyclic society) will start no later than in the middle of 2019. The breakthough in AI and technologies can prevent its start but it is unlikely considering the present trends and development that such breakthrough can happen until then.

Apparently – there will be very sad consequences of this crisis. Firstly – investment will be slashed and obviously the AI community will experience the biggest layoufs due to unfulfilled promises. The society as a whole will experience setback. Even at preset there are critics of AI from the less educated circles – they don’t believe that the manual work and low level human work (like call centers, accounting, law clerks) can be automated and hence they don’t believe in investments in AI and robotics research. If there will be crises, then businesses will abandon costly and uncertain investments and will rely on the manual and primitive work of humans. And hence – the worldview of the less educated will govern the political agenda and this even more will hamper the AI research funding and AI investments.

So – due to coming economic crisis and due to roadblocks – it is quite obvious that AI community action is required to redefine the AI research efforts, methods, the realign the funding, to advise the research strategies of the global companies that are biggest AI investors, to give sound advise to the governmental funding agencies. The question is – is there such AI community action, panel of researchers, action by the leading professional societies or NGOs which can guarantee the survival of AI during the next economic crisis and which could prevent the second (and maybe final) AI winter. Is there such action?

This question is in the name of the happy future of humanity.

I hope this is not a duplicate, I saw this question:
How long before PhD graduation should I start applying for post-doc positions?

However, I didn’t find any analogous question for applying for jobs (academic). Is there a certain timeline to look for jobs?

In particular, do I need to have completed the PhD or fulfill any requirement (e.g. submitted thesis) before I can look for jobs?

I am considering any kind of academic job, tenured, or non-tenured jobs like lecturer and adjunct posts.

I am looking at mid-tier universities in Asia, in applied mathematics and related fields.

I’m a recently admitted in a masters program in mathematics. My question is about “strategy” of learning. Should I focus on learning many things or should I pick an research area and start learning only stuff about that area? This is a problem because some scholars told me that if I want to get hired in academia, I must start publishing as early as possible so I better start to deepen my knowledge in certain area. However, actually I have not decided with area I fit the most, and I think that broadness is useful as well, because diversity is a necesity for creativity, just as professional musicians doesn’t just hear say classical music, but rock music, or latin music to get inspiration, I think that mathematicians must not just read about their own area, but related areas as well.

In your experience, which “strategy of learning” is better for a begginer in graduate school? I must say that I have freedom for choose courses.